Predictive glycoengineering of biosimilars using a Markov chain glycosylation model

Abstract

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, we describe a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.

Glycoengineering in CHO Cells: Advances in Systems Biology

“…Mathematical models provide an alternative to experimental investigations in addressing the effects of cellular or process changes introduced to increase recombinant protein productivity or maximize desired glycoform fractions within the glycoform population. As mathematical models have advanced, their predictive powers have increased in correctly guiding cellular engineering efforts, reducing the time required to develop a cell line producing desired glycosylation patterns . The major mathematical models developed to address the N‐glycosylation process and aid the cellular engineering efforts are described below in chronological order according to the year of development.…”

“…The model utility in predictive glycoengineering applications in CHO cells was shown by its ability to accurately predict the effects of gene knockouts for GnT‐IV and core FucT on rhEPO and IgG, respectively. In another study, Spahn et al demonstrated that the algorithm can successfully predict the experimental perturbations required to replicate the glycosylation pattern in biosimilar versions of two glycoprotein therapeutics.…”

The glycosylation aspects of biosimilarity

“…Predictive glycoengineering can also help in the development of biosimilars. For example, the computational method using the Markov chain model of glycosylation can predict the quantitative amounts (e.g., nutrients, inhibitors, and enzymes) by which glycosylation reaction rates must be perturbed to obtain a specific glycosylation profile [51]. Brühlmann et al developed a parallel design-of-experiment (DoE) approach.…”

Quantum Modeling: A Bridge between the Pumping and Signaling Functions of Na/K-ATPase

“…First, we assume that confrontational change of a single Na/K-ATPase unit is simply (and sometimes incorrectly) independent from adjacent conformations during its turnover process, therefore, its turnover process could be depicted by the Markov chain model [27]. Obviously, at the equilibrium state, this single Na/K-ATPase unit will essentially represent a superposition of a number of conformations which can be described as follows:$\{X_n,n\in T\}(T=0,1,2,\dots )$…”