PEGylation is a well-established and clinically proven half-life extension strategy for protein delivery. Protein modification with aminereactive poly(ethylene glycol) (PEG) generates heterogeneous and complex bioconjugate mixtures, often composed of several PEG positional isomers with varied therapeutic efficacy. Laborious and costly experiments for reaction optimization and purification are needed to generate a therapeutically useful PEG conjugate. Kinetic models which accurately predict the outcome of socalled "random" PEGylation reactions provide an opportunity to bypass extensive wet lab experimentation and streamline the bioconjugation process. In this study, we propose a protein tertiary structure-dependent reactivity model that describes the rate of protein-amine PEGylation and introduces "PEG chain coverage" as a tangible metric to assess the shielding effect of PEG chains. This structure-dependent reactivity model was implemented into three models (linear, structure-based, and machine-learned) to gain insight into how protein-specific molecular descriptors (exposed surface areas, pK a , and surface charge) impacted amine reactivity at each site. Linear and machine-learned models demonstrated over 75% prediction accuracy with butylcholinesterase. Model validation with Somavert, PEGASYS, and phenylalanine ammonia lyase showed good correlation between predicted and experimentally determined degrees of modification. Our structure-dependent reactivity model was also able to simulate PEGylation progress curves and estimate "PEGmer" distribution with accurate predictions across different proteins, PEG linker chemistry, and PEG molecular weights. Moreover, in-depth analysis of these simulated reaction curves highlighted possible PEG conformational transitions (from dumbbell to brush) on the surface of lysozyme, as a function of PEG molecular weight.

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Recombinant proteins represent almost half of the top selling therapeutics—with over a hundred billion dollars in global sales—and their efficacy and safety strongly depend on glycosylation. In this study, we showcase a simple method to simultaneously analyze N‐glycan micro‐ and macroheterogeneity of an immunoglobulin G (IgG) by quantifying glycan occupancy and distribution. Our approach is linear over a wide range of glycan and glycoprotein concentrations down to 25 ng/mL. Additionally, we present a case study demonstrating the effect of small molecule metabolic regulators on glycan heterogeneity using this approach. In particular, sodium oxamate (SOD) decreased Chinese hamster ovary (CHO) glucose metabolism and reduced IgG glycosylation by 40% through upregulating reactive oxygen species (ROS) and reducing the UDP‐GlcNAc pool, while maintaining a similar glycan profile to control cultures. Here, we suggest glycan macroheterogeneity as an attribute should be included in bioprocess screening to identify process parameters that optimize culture performance without compromising antibody quality.