Discussion
Herein we present a novel approach to utilize amino acid stoichiometric
balances as inputs into MVDA models such as the orthogonal partial least
squares algorithm to balance key amino acids in chemically-defined cell
culture media supplements to increase CHO cell growth and productivity.
However, the utilization of stoichiometric balances in chemometric
approaches is not novel. Experimental stoichiometric models date as
early as 1994 where Xie and Wang designed a cell culture medium
development strategy in which the key nutrients required in biomass and
product generation could be described mathematically by stoichiometric
coefficients (Xie & Wang, 1994b; Xie & Wang, 1994c). Since increased
cell mass is a necessary requirement to produce the desired
concentration of key biologic products such as mAbs, the specific cell
growth rate (ugrowth) and specific productivity (qP)
become two key governing parameters within the stoichiometric equation.
The resulting stoichiometric balance would help derive the theoretical
demand of each nutrient, and thus, Xie and Wang showed that various
nutrients could be balanced within complex CD media without the
necessity of multi-factorial DoEs.
For instance, in a follow up study, Xie and Wang used their
stoichiometric model to decrease ammonia and lactate formation in
hybridoma cells by balancing the basal medium nutrient levels. In this
model, they analyzed the relationship between glutamine and nonessential
amino acids as a function of lactate and ammonia production rates. The
modified medium resulted in a 4-fold decrease in ammonia production and
almost a 10-fold decrease in lactate formation from the hybridoma cells
with the added benefit of increased cell growth and product formation
(Xie & Wang, 1994a). Val et al utilized the stoichiometric model to
balance nucleotide sugars as a function of cell growth and specific
productivity to understand nucleotide sugar demand towards glycosylation
profiles in CHO cells (Del Val, Polizzi, & Kontoravdi, 2016). Xie and
Nyberg et al further utilized the stoichiometric modeling approach in
CHO cells by designing a serum-free feed medium with balanced nutrients
to improve cell growth and IFNy productivity as well as glycosylation
efficiency for IFNy (Xie et al., 1997).
Similarly, we derived stoichiometric balances to improve cell growth and
productivity as a function of all 20 amino acids. In agreement with the
original method, the stoichiometric coefficients or theoretical demands
for amino acids derived in our model were based on their contributions
towards biomass and mAb productivity as a function of specific growth
rate (µgrowth) and specific productivity (qP).
Accordingly, Xie and Wang’s original method also considered the cell
death rate, thus resulting in gross growth rate (Xie & Wang, 1994c).
Cell death rate becomes particularly important during the
late-stationary and death phases of the CHO cell culture where viable
cell densities and viabilities start to decline (Balandras et al.,
2011). The increased death rate during the latter half of the culture is
attributed to several factors including increased concentration of
harmful byproducts such as NH3, increased packed cell volume, and rapid
nutrient elimination (Pan, Dalm, Wijffels, & Martens, 2017b; Pascoe,
Arnott, Papoutsakis, Miller, & Andersen, 2007). Death rates as a
result, are either calculated quantitatively by measuring differential
levels of extracellular LDH accumulation that is released from lysed or
apoptotic cells or theoretically by adjusting the net growth rate by a
factor correlating to viability decline (Martínez, Bulté, Contreras,
Palomares, & Ramírez, 2020; Martins Conde Pdo, Sauter, & Pfau, 2016;
Templeton et al., 2017). Although the net growth rate was used in our
calculation for theoretical demand, the growth model only predicted
important variables up to day 9 in the culture at which point the
viabilities of the cultures were relatively similar suggesting minute
differences between gross and net growth rates. The resulting
stoichiometric balance solution was obtainable by taking the difference
between the empirical consumption and the theoretical demand of every
amino acid. In all cases however, the underlaying assumption behind
stoichiometric balances was that the intracellular metabolism and
complex gene expression machinery could be generalized into a “black
box” in which extracellular fluxes of nutrients from the media could be
directly related to biomass and product generation.
Statistical models in bioprocess have also relied on the “black box”
approach in correlating extracellular metabolites and fluxes to cellular
phenotypical behavior (Kroll, Hofer, Ulonska, Kager, & Herwig, 2017;
Lee & Gilmore, 2006). In addition, multivariate approaches also offer
the added benefit of being able to highlight key variables that
contribute towards the prediction of specific response variables
(Akarachantachote et al., 2013; Gangadharan et al., 2019; Hassan,
Farhan, Mangayil, Huttunen, & Aho, 2013). Applied together with the
stoichiometric model, the complexity of balancing all 20 amino acids in
CD media can be greatly reduced by balancing only the specific amino
acids deemed important by the statistical model. Furthermore,
statistical models are easier to develop, do not require systematic
constraints or curation of biological parameters, and are
computationally efficient in determining key correlations (Kim, Rocha,
& Maia, 2018; Martins Conde Pdo et al., 2016). More recently however,
there has been keen interest on obtaining a deeper understanding of the
effects on the metabolic framework by stoichiometric balancing via
mechanistic modeling (Sha, Huang, Wang, & Yoon, 2018). Such models can
elucidate the intricacies of the intracellular pathways that are
extrapolated within statistical models. Thus, mechanistic models can
help identify causal linkages within correlation structures. In a
modeling review, Traustason et al described several computational
methods aimed at optimizing amino acid concentrations for CD media in
CHO cell culture. Among them include stoichiometric models such as
metabolic flux analysis (MFA) or flux balance analysis (FBA) as well as
kinetic models (Traustason, B., Cheeks, & Dikicioglu, 2019). Robitaille
et al developed a dynamic model combining the benefits of MFA and
kinetic models to explain the relationship between extracellular fluxes
of amino acids, extracellular concentrations, and the intracellular flux
responses towards that consumption. With a hybrid steady-state and
kinetic model, a dynamic relationship between nutrient uptake and
cellular response in terms of biomass generation and mAb production was
determined (Robitaille, Chen, & Jolicoeur, 2015b).
In another example, combination of FBA and genome-scale modeling with
the utilization of amino acid stoichiometric balancing revealed that the
addition of nonessential amino acids (NEAAs) have a positive impact on
CHO cell biomass production (Traustason, Bergthor, 2019). CHO cells are
reported to contain seven NEAAs including glycine, alanine, asparagine,
aspartic acid, glutamic acid, proline, and serine (Fomina-Yadlin et al.,
2014). These seven amino acids are nutrients that CHO cells can directly
biosynthesize whereas the remaining 13 essential amino acids must be
obtained by nutrient supplementation from the CD media (Salazar et al.,
2016). Presumably, the supplementation of NEAAs can redirect the energy
needed from biosynthesis of amino acids to other biological processes
such as cell growth or protein synthesis. Moreover, several essential
amino acids are also used in biosynthesis for NEAAs and other metabolic
intermediates, suggesting that supplementation of NEAAs could not only
free up energy but also free up nutrients to support the biosynthesis of
other cellular processes (Duarte et al., 2014; Harcum & Lee, 2016).
Accordingly, the significant increases of cell growth and mAb
productivity that were observed within moderate and low nutrient
conditions for both the growth and production models in Criterion 2
supported the energy conservation theory of NEAAs since two of the three
amino acids supplemented for cell growth were NEAAs. Supplementation of
alanine and glycine also showed increased consumption in all model
conditions which also resulted in a decreased specific consumption of
glucose further suggesting that the conserved energy was shifted towards
cell growth and increased cellular metabolism. In the case of mAb
productivity, methionine and lysine was also supplemented. Although not
counted within NEAAs, methionine plays an important role in protein
production as it is the universal initiator of protein synthesis and
thus, crucial to cellular uptake and metabolism to increase protein
production. In addition, methionine has shown to improve the cellular
redox state of cells by using the ubiquitous methionine sulfoxide cycle
to provide an antioxidant defense system (Lim, Kim, & Levine, 2019).
Accordingly, we presume that the supplementation of NEAAs, critical
protein synthesis amino acids, and increased cell growth all contributed
towards the significant increase in mAb productivity within moderate and
low nutrient model conditions.
In the case of high nutrients feeds, although there was a marked
increase in cellular consumption of almost all amino acids, there was
only a minute increase in cell growth and mAb productivity, and we
presumed that a saturation point in the culture was reached. However,
the notion of a saturation point with respect to extracellular amino
acid concentrations was supported by Salazar et al who described that
supplementing concentrations of amino acids that are generally highly
consumed in mammalian cell cultivation does not always lead to
improvement in cell culture, but rather can lead to undesired effects
and the accumulation of harmful waste products (Salazar et al., 2016).
Similarly, Templeton et al experimentally validated previous MFA models
with 13C labeling studies in vitro and showed
that protein expression does not always correspond the activation of
specific metabolic pathways, but rather, even fluxes tend to eventually
saturate (Templeton et al., 2017b). Taken together, high nutrient
conditions resulted in greater consumption but maintained similar
specific glucose consumption rates suggesting that cellular energy was
not repurposed to increased cell growth or mAb productivity, rather, the
cells had reached their maximum capacity to metabolize amino acids.