(299c) Towards Controlling the Glycoform: Bridging the Gap from the Extracellular Environment to Antibody Glycosylation

Glycoproteins are the largest and most valuable group of biologically-derived medicines and account for 77 of the 643 therapeutics approved by the EMA [1]. These large glycoprotein molecules are characterised by their glycan moieties, which are complex post-translation modifications formed by a series of enzymatic reactions in the ER and the Golgi. The synthesis of glycan structures is driven by the availability of nucleotide sugar donors (NSDs), which are the co-substrates to the glycosylation process. As a result of this the distribution of glycan structures is strongly influenced by process conditions such as temperature, nutrient availability, dissolved oxygen, operation schedule and culture mode. This work focuses on nutrient availability as well as feeding of NSD precursors to the culture medium, which are known to influence intracellular availability of NSDs, which in turn is known to impact the glycan profile of biotherapeutics [2]. In the context of biotherapeutics glycan structures are known to play an important role in pharmacokinetics such as serum half-life, therapeutic efficacy and most importantly drug safety.

In this work a modelling platform has been developed linking the extracellular environment via intracellular metabolites and their availability in the cytoplasm and Golgi to the glycosylation of the heavy chain of a monoclonal antibody [3]. The presented dynamic modelling framework is split into four interlinked parts: 1) a cell growth model based on modified Monod kinetics, 2) a simplified purine and pyrimidine synthesis network, 3) a mechanistic representation of the nucleotide sugar donor synthesis pathway and 4) a model describing the N-linked glycosylation of the antibody Fc region as developed by del Val et al [4]. Particular attention was paid to the mammalian NSD pathway by means of a bottom-up mechanistic in silico reconstruction of the network. The model comprises 34 mass balances corresponding to the equivalent number of species that make up the metabolic network and the balances are connected through a total of 60 individual reaction rates.  All reaction steps and NSD transporter outlets were modelled as saturation kinetics and described as individual rate expressions based on enzyme mechanisms and kinetics found in the literature. Where necessary model parameters were estimated from experimental data. This included cell growth dynamics, extracellular nutrient availability, dynamic intracellular NSD data, antibody titer and the antibody product glycoform.

The result of this study is a modelling framework, which is able to link the extracellular environment via intracellular metabolites to the glycosylation profile of the IgG product. The linked outputs of all parts of the model are in good agreement with experimental data and the model was able to capture the dynamic impact of nucleotide and sugar additions to the media on the availability of intracellular NSDs. Ultimately this modelling framework would allow to assess the impact of genetic engineering and process operation strategies such as NSD precursor feeding strategies in silico and guide experimentation and finally process development. This framework is a first step towards a platform for the in silico optimisation of bioprocess conditions with respect to product quality and safety in line with the Quality by Design (QbD) paradigm.

While this model describes the dynamic distribution of the conserved glycan of the IgG heavy chain, the modular nature of the framework allows it to be coupled with any other dynamic model using NSD flux and/or concentrations as input. Furthermore the modular nature will allow for the framework to be easily translated to other operation modes, culture conditions or expression systems.

References

1.            Kyriakopoulos, S.; Kontoravdi, C., Analysis of the landscape of biologically-derived pharmaceuticals in europe: Dominant production systems, molecule types on the rise and approval trends. Eur J Pharm Sci 2013, 48, 428-441.

2.            P.M., J.; I., J.d.V.; K.M., P.; C., K., Applying quality by design to glycoprotein therapeutics: Experimental and computational efforts of process control. Pharmaceutical Bioprocessing 2013, 1, 51-69.

3.            Jedrzejewski, P.M.; del Val, I.J.; Constantinou, A.; Dell, A.; Haslam, S.M.; Polizzi, K.M.; Kontoravdi, C., Towards controlling the glycoform: A model framework linking extracellular metabolites to antibody glycosylation. International journal of molecular sciences 2014, 15, 4492-4522.

4.            del Val, I.J.; Nagy, J.M.; Kontoravdi, C., A dynamic mathematical model for monoclonal antibody n-linked glycosylation and nucleotide sugar donor transport within a maturing golgi apparatus. Biotechnol Progr 2011, 27, 1730-1743.