Strategies for controlling afucosylation in monoclonal antibodies during upstream manufacturing

Abstract

Core fucosylation is a highly prevalent and significant feature of N-glycosylation in therapeutic monoclonal antibodies produced by mammalian cells where its absence (afucosylation) plays a key role in treatment safety and efficacy. Notably, even slight changes in the level of afucosylation can have a considerable impact on the antibody-dependent cell-mediated cytotoxicity. Therefore, implementing control over afucosylation levels is important in upstream manufacturing to maintain consistent quality across batches of product, since standard downstream processing does not change afucosylation. In this review, the influences and strategies to control afucosylation are presented. In particular, there is emphasis on upstream manufacturing culture parameters and media supplementation, as these offer particular advantages as control strategies over alternative approaches such as cell line engineering and chemical inhibitors. The review discusses the relationship between the afucosylation influences and the underlying cellular metabolism to promote increased process understanding. Also, briefly highlighted is the value of empirical and mechanistic models in evaluating and designing control methods for core fucosylation.

An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality

“…In addition to machine learning approaches such as black box models used in this study, mechanistic models are also developed but only for individual product quality like glycans and charge variances. [ 7,9 ] These mechanistic models usually require additional process information and complex parameter estimation. In contrast, our model uses only traditional inputs and therefore any product quality attribute as Y variable can be modeled.…”

“…Glycosylation has been modeled in detail, allowing a better understanding of glycoform biosynthesis. [ 7–9 ] However, the amount of prior knowledge and the need to create new knowledge for more complex drug targets strongly limits this approach.…”

Standardized approach for accurate and reliable model development of ion‐exchange chromatography based on parameter‐by‐parameter method and consideration of extra‐column effects

“…Subsequently, σ and k kin were determined by solving the optimization problem defined in Equation (17). The results were σ A1 = 36.0, σ A2 = 67.1, and σ Main = 15.5.…”

“…[13][14][15][16] The implementation holds promise in effectively reducing the time to market during the development of new biopharmaceuticals. [17] However, applying these models in industrial chromatography still presents several challenges, with one of the most significant being the development of accurate and reliable models.…”