S-Glyn™ : Optimizing N-Glycan Profiles and their Effector Functions Using a Design-of-Experiments Approach

S-Glyn™ : Optimizing N-Glycan Profiles and their Effector Functions Using a Design-of-Experiments Approach

December 14, 2023PAO-12-23-CL-06

Mammalian glycan structures are closely linked to immune functions. With the growing understanding of the relationships between abnormal glycan profiles and human diseases, glycoengineering has been widely adapted for antibody therapeutic development in oncology and immunology. CMC development with such antibody therapeutics from gene to Investigational New Drug (IND) application is often challenged by stringent timelines. Identifying the myriad posttranslational modifications (PTMs) that can impact safety and efficacy is a complex and time-consuming aspect of that journey. Thoughtful application of design-of-experiments (DoE) strategies can significantly accelerate this activity. Here, we describe a DoE approach for evaluation of N-glycan profiles resulting from glycosylation and the relevant antibody effector functions that can impact drug performance. This work can be completed alongside process development and clinical material manufacturing, thereby reducing development timelines.

Need to Accelerate Glycosylation Profile Analysis

PTMs are chemical modifications to proteins and antibodies that impact the functioning of the biotherapeutic. They can occur during cell culture or in vivo as part of the natural biochemical processes in which the protein or antibody participates. Over 400 types of PTMs have been identified, affecting a host of different protein functions,1 including the addition of chemical and peptidic moieties, proteolytic cleavage of different subunits, and protein degradation. Examples of PTMs include phosphorylation, glycosylation, ubiquitination, nitrosylation, methylation, acetylation, and lipidation. Twenty-four of these types are considered to be major and are known to affect at least 80 different protein sites.2

Protein glycosylation is one of the most prominent and well-characterized PTMs and is predominantly determined by host-cell characteristics and cell culture conditions. In glycosylation, a sugar moiety is added to either nitrogen (N), oxygen (O), or carbon (C) atoms within the protein structure, which can impact protein folding, conformation, distribution, stability, and activity, including cell adhesion, cell–cell and cell–matrix interactions, molecular trafficking, receptor activation, and signal transduction, among other cellular activities.1 The sugar group can vary from a simple monosaccharide to complex, highly branched polysaccharides.

Given that glycosylation during cell culture can directly impact the biological activity of the drug substance, it is essential to identify and characterize these PTMs and determine which glycosylations have positive or negative impacts on drug performance. However, detection, purification, and identification of the myriad glycosylation PTMs in a single antibody is a complex and time-consuming task. Accelerated development timelines warrant rapid and more efficient strategies to optimize glycosylation profiles during upstream process development.

Value of a Design-of-Experiments Approach to Optimization

DoE leverages statistical analyses to evaluate the results of sets of experiments in which multiple parameters are varied simultaneously. The multivariate approach enables simultaneous evaluation of both the interdependent and dependent impacts of the different parameters on outcomes.

In the biopharma industry, DoE enables more rapid identification of critical process parameters (CPPs) and critical quality attributes (CQAs), thus accelerating process development while also designing quality into processes from the outset (i.e., supporting quality-by-design (QbD) strategies). In addition, DoE affords greater process knowledge and a better understanding of the full process design space.

Identifying and characterizing PTMs is an ideal application for the DoE approach given the complexity of the PTM landscape. This strategy can be highly beneficial when determining glycosylation profiles in particular, which can involve bonding of different sugar moieties with N, O, and C atoms on a host of amino acids at many different sites within antibodies and proteins. Indeed, DoE and statistical analysis can be used to swiftly optimize and predict glycosylation patterns while deepening process knowledge and understanding.

DoE Study for N-Glycan Optimization

Samsung Biologics has developed a DoE approach to the optimization of N-glycan profiles and their effector functions. The method was demonstrated for two different antibodies.

First, cells from two distinct cell lines producing the two different antibodies (a fusion-derived monoclonal antibody (mAb) and a traditional mAb) were cultured using the Ambr® 15 Advanced Microbioreactor System (Sartorius). DoE parameters were established based on culture process parameters and additives for glycan control. The temperature was adjusted on Day Six, and additives for N-glycan modulation were introduced on Day Seven. The antibodies were purified using Protein A affinity chromatography, denatured, and subsequently deglycosylated using the PNGase F enzyme (New England Biolabs GmbH).

The liberated N-glycans were labeled using a 2 AB labeling kit (Ludger). Glycan profile analysis for the released glycans was conducted using ultra-performance liquid chromatography (UPLC). Desirable glycans were analyzed further using JMP® 17.0 (SAS Inc., Cary, NC) software. Response surface models using multiple linear regression were fitted to determine the statistical significance of selected parameters, their quadratic terms, and their interaction for the N-glycan modulation.

In addition, antibody products generated under the different DoE conditions were further purified and analyzed for N-glycan modulation to identify individual N-glycan peaks prior to N-glycan analysis. Cell growth data including titer were then further analyzed statistically in combination with the N-glycan results to identify the optimum cell culture conditions for maximizing yield and N-glycosylation.

Effector Function Determination

In addition to identifying optimum cell culture parameters, the potential impacts of N-glycans on antibody functionality were investigated. Studies were performed to determine whether the N-glycans could be potential CQAs via their influence on effector functions of antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). Two levels of relevant glycan samples were used to identify the correlations between each glycan and the effector functions, which were demonstrated to be positive for the two antibodies.

ADCC is a cytosolic function in which effector cells with cytotoxic potential lyse target cells. CDC is a mechanism by which antibodies mediate target cell lysis through activation of the complement system. Both were assessed using cell-based bioluminescent (reporter and viability, respectively) assays, with activity levels determined by preparing dose-response curves and calculating the parameters using four-parameter logistic (4-PL) regression.

For ADCC assessment, target cells were plated onto an opaque white 96-well assay plate that was then pre-incubated overnight. Subsequently, different doses of the antibodies and effector cells were added to the assay plate and incubated. Luminescence was measured following the addition of a luciferase detection reagent.

For CDC assessment, reference material, test samples, and target cells were added to the assay plate, which was then incubated. Subsequently, 5% BRS (Baby Rabbit Serum) was added and the plate further incubated. Following the addition of the Cell-Titer Glo® substrate (Promega), luminescence was measured.

Both ADCC and CDC were determined via comparison of the dose-response curves and parameters calculated from the 4-PL curve.


Case Study: N-Glycan Optimization of a Fusion-Derived Monoclonal Antibody
Notably, all of the different cell culture conditions (18 in total) enabled successful cell culture without significant inhibition of cell growth. Indeed, the majority of conditions produced cells with over 90% viability.
Individual N-glycan peaks were identified using UPLC prior to glycan profile analysis. All results were statistically analyzed using stepwise regression to find the right conditions for N-glycan modulation. Three different graphs are presented in Figure 1. They include (A) actual vs. predicted afucosylation, (B) the correlation between afucosylation and different cell culture parameters, and (C) the prediction model indicating the values and conditions expected to provide the desired N-glycan profile. Overall, these analyses revealed the process parameters that are statistically significant and those that result in the production of drug substance with the desirable N-glycan profile.
Statistical analysis of N-glycan profiles predicts optimal conditions



Figure 1. Statistical analysis of N-glycan profiles (A) Plot showing actual vs. predicted afucosylation with analysis of variants; (B) Interaction profile showing the correlation between afucosylation and the various cell culture parameters; (C) Prediction model showing the expected values and conditions that meet the desirable profile.
With respect to ADCC and CDC activity, analyses of relevant effector functions for antibodies with different N-glycan profiles were performed. As expected, elevating the levels of afucosylation and galactosylation on the fusion-derived antibodies led to the augmentation of ADCC (Figure 2) and CDC (Figure 3), respectively.
High afucosylation/galactosylation augments ADCC/CDC effector functions


Figure 2. Summary of ADCC results. (A) Dose-response curve showing differences in efficacy between the reference, high-afucosylated, and low-afucosylated antibodies; (B) Relative maximum ADCC values of low- and high-afucosylated antibodies; (C) EC50 values of low- and high-afucosylated antibodies.


Figure 3. Summary of CDC results for high, middle and low galactosylated antibodies. (A) Dose-response curve showing differences in efficacy; (B) Maximum CDC values; (C) EC50 values
It should be noted that similar results were obtained for the DoE study of the traditional mAb. Statistical analyses of N-glycan profiles and relevant effector functions were performed. As expected, higher afucose levels increased ADCC activity, while higher galactosylation levels increased CDC activity.


Greater Process Understanding without Extending Development Timelines

The development journey of antibody therapeutics, from gene to IND, is often challenged by stringent timelines despite the need to thoroughly understand how process parameters impact PTMs, impurity profiles, and other CQAs. The case studies outlined herein demonstrate that, at Samsung Biologics, N-glycan profiles and relevant antibody effector functions can be successfully evaluated in a time-effective manner alongside process development and clinical material manufacturing using a DOE approach. Because it is possible to perform this type of DoE study concurrently with other contract development services, extension of the current development timeline, which is approximately 11 months, can be avoided. Importantly, this approach can provide customers with a clearer understanding of the optimal N-glycan status and potential CQAs of their products across development phases.



  1. Ramazi, Shahin and Javad Zahiri.Post-translational modifications in proteins: resources, tools and prediction methods.” Database (Oxford). 7 Apr. 2021.
  2. Huang, K.-Y., T.-Y. Lee, H.-J. Kao, et al.dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications.” Nucleic Acids Res. 47: D298–D308, (2018). https://doi.org/10.1093/nar/gky1074
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