Abstract
N-linked glycan distribution plays a significant role in the generation of therapeutic proteins. It is challenging to determine the operating conditions when developing a new biopharmaceutical product with the desired glycan distributions. The glycosylation is a high complex nonlinear system, and it is difficult to develop a reliable first-principle model that heavily relies on experimentation. Our goal is to develop a nonlinear data-driven model and find an appropriate operating space included kinds of input combination from process variables based on this model to ensure the desired product quality. A methodology is proposed based on the inversion of a nonlinear latent-variable model (locality preserving projection to latent structures, LPPLS) to identify a subspace of the knowledge space. The normal operating points of the input variables are designed based on the LPPLS inversion, and the range of operating conditions are expanded around the normal operation points through the prediction uncertainty analysis of forward and inversion model simultaneously. Finally, the designated operation space from LPPLS inversion is applied in an benchmark glycosylation model.
Highlights
The monoclonal antibodies play an important role in biomedical science as a glycosylation end products, since the products of the glycosylation process are directly associated with the therapeutic effects of related proteins
The results showed that LPPLS is more accurate compared with projection to latent structures (PLS), that means the nonlinear method of searching glycosylation process operating space is effective
In this article, we have developed a methodology based on local preserving projections latent variable in inversion form to determine the process operating conditions of desired quality specifications
Summary
The monoclonal antibodies play an important role in biomedical science as a glycosylation end products, since the products of the glycosylation process are directly associated with the therapeutic effects of related proteins. The space of operating conditions can be determined through the appropriate model between the process variable and product quality. J. Wang et al.: Narrow Operating Space Based on the Inversion of Latent Structures Model for Glycosylation Process experiment in which several different methods were used to find the operating conditions for various data features [16]. LPPLS method is used to provide insight into the internal nonlinear interaction of the inputs (bio-enzymes) and outputs (various glycan classes or specific glycoforms) for glycosylation process. Different from the current approaches, a narrow operating space is located inside the given knowledge space under the prediction uncertainty both in the forward and inverse LPPLS models. The highlights of our work include, (1) A methodology is presented to determine the operating space for a new biopharmaceutical product manufactured from the protein glycosylation process in vivo. We use LPPLS to extract the maximum relevant information between glycosylation enzymes and glycan outputs in glycosylation process
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