Introduction
Improved robustness in production of monoclonal antibodies (mAbs) from
Chinese Hamster Ovary (CHO) cell culture processes depends on gaining
better understanding of biological changes, interactions, and impact to
product quality and yield. Advances in systems biology have led to the
development of computational tools to compliment emerging technologies
that can perform data-rich experimentation (Schaub, Clemens, Kaufmann,
& Shulz, 2011; Sharma, Tripathi, & Mukherjee, 2016). As a result, the
employment of computational tools such as empirical or mathematical
modeling have gained acceptance as a potential path to provide a deeper
understanding of cellular mechanisms to inform process decisions that
result in more efficient experimental practices during pre-commercial
biologics development (Insaidoo, Banerjee, Roush, & Cramer, 2017; Roush
et al., 2020; Smiatek, Jung, & Bluhmki, 2020). For instance,
computational efforts have shown great potential in optimizing nutrient
concentrations within chemically defined (CD) media for CHO cells by
modeling routine cell culture and metabolomic data to highlight key
limiting nutrients (Cuperlovic-Culf & Culf, 2016). Optimizing base and
feed media compositions is a critical step of process development as
components in these media directly impact critical quality attributes
and cellular productivity of mAbs (Li, Vijayasankaran, Shen, Kiss, &
Amanullah, 2010). However, despite long-term benefits of raw material
control and cost reduction, development of proprietary CD media is a
resource-and time-intensive process which requires a significant number
of experiments (Kelley, 2009). By leveraging historical data in
computational algorithms, process development researchers are able to
identify key components in media that can significantly reduce the
complexity of large experimental designs and reduce the development time
to optimize media (Torkashvand et al., 2015; Xing et al., 2011).
In this study, we explored the utilization of multivariate data
analytics (MVDA) modeling to optimize amino acid concentrations in CD
feed media with aims to increase cell growth and mAb productivity. While
other components in CD media such as vitamins, trace elements, and
co-factors are required for various cellular functions, amino acids are
considered one of the most important component group in CD media
representing about 74% of CHO cell mass (Carrillo-Cocom et al., 2015;
Fan et al., 2015; Pan, Streefland, Dalm, Wijffels, & Martens, 2017;
Templeton, Xu, Roush, & Chen, 2017a). Balancing and optimizing amino
acid concentrations therefore is critical to ensure CD media can sustain
cell growth and mAb productivity. Thus far, majority of laboratory-based
experimental and computational approaches for optimizing amino acid
levels in CD media have been built on supplementing the amino acids that
were being consumed in excess (Dietmair et al., 2012; Duarte et al.,
2014; Robitaille, Chen, & Jolicoeur, 2015a). A challenge with such an
approach is that over supplementation and consumption of certain amino
acids can lead to the production of harmful byproducts. For instance,
varying the amount of essential amino acid concentrations can lead to
the differential regulation of amino acid metabolic pathways that could
ultimately result in increased formation of harmful byproducts such as
lactate or ammonia (Alden et al., 2020; Morris et al., 2020; Park,
Reimonn, Agarabi, Brorson, & Yoon, 2018; Pereira, Kildegaard, &
Andersen, 2018). Alternatively, supplementing amino acids in a
time-dependent manner based on cell demand would ensure optimal
consumption and cellular metabolism presumably resulting in better cell
growth and mAb productivity.
In aims to understand the dynamics of amino acid metabolism, Xie and
Wang described the relationship between the theoretical demand and the
actual consumption of amino acids as their stoichiometric balances (Xie
& Wang, 1994b; Xie & Wang, 1994c). According to their work, the
theoretical demand of an amino acid is the amount needed to maintain
both biomass and mAb productivity per cell, and with a steady state
approximation, the amount consumed is balanced stoichiometrically with
the theoretical demand. However, recent work on CHO cell amino acid
requirements showed that most cells do not behave in a steady state and
that the production process by which nutrient feeds are provided can
greatly modulate the dynamics of consumption (Kyriakopoulos, Polizzi, &
Kontoravdi, 2013; Luo et al., 2020). Templeton et al described the
existence of two possible metabolic states. Within the first state, the
actual consumption of an amino acid exceeds the theoretical demand
resulting in a catabolic state in which the cell can breakdown the
excess nutrients for energy or biomass. In contrast, within the second
state, the theoretical demand exceeds the actual consumption of an amino
acid resulting in an anabolic state in which the cells must
biosynthesize or acquire the required nutrients (Table 1) (Templeton,
Xu, Roush, & Chen, 2017b). Time-dependent dynamics of amino acid
stoichiometric balances can therefore highlight concentration
constraints and metabolic requirements for different amino acids
throughout the culture (Ahn & Antoniewicz, 2011; Pan, Dalm, Wijffels,
& Martens, 2017a). Although a stoichiometric-based model can provide a
systematic and mathematical approach to balancing component composition
in CD media, it does not eliminate the resources, and time needed for
experimental work (Kumar, Bhalla, & Rathore, 2014). Accordingly,
computational MVDA approaches have shown potential in utilizing
historical cell culture data and nutrient consumption rates to minimize
the resource demand of lab-based experimental designs and execution
(Suarez-Zuluaga, Borchert, Driessen, Bakker, & Thomassen, 2019).
MVDA methods rely on a dimensional reduction strategy in which
information from a group of variables can be consolidated and summarized
into latent variables that in turn, can help describe the overall
distribution of experimental observations (i.e. culture time points,
cell culture batches) (Mevik & Wehrens, 2007). Each latent variable is
based on the individual contribution of variance from all of the
variables within the group, and variable ranking statistics can
therefore be used to highlight and sub-select a set of key variables
towards specific response parameters (Prieto, Eriksson, & Tyrgg, 2014).
As a result, MVDA approaches can be used to analyze batch-to-batch
variability, test the impact of culture performance from process
variations, and identify key nutrients from cell culture media (Nitish
& Rathore, 2011). In addition, statistical MVDA models in bioprocess
rely on the “black box” approach in correlating extracellular
metabolites and fluxes to cellular phenotypical behavior without
mathematically modeling the intracellular complexities of cellular
metabolism (Kanehisa & Goto, 2000; Noll & Henkel, 2020). Accordingly,
MVDA models are easier to develop, do not require systematic constraints
or curation of biological parameters, and are computationally efficient
in determining key correlations (Rouiller et al., 2013).
Several MVDA algorithms that are routinely used within bioprocess
development include Principal Component Analysis (PCA), Partial Least
Squares (PLS) Regression, and variations of PLS such as Orthogonal
Partial Least Squares (OPLS) (Mevik & Wehrens, 2007; Yamamoto et al.,
2009). For instance, Kirdar et al utilized PCA to diagnose similarities
and differences between small-scale and large-scale manufacturing
batches for biopharmaceutical products (Kirdar, Conner, Baclaski, &
Rathore, 2007). Dietmair et al utilized PLS to determine a range of
metabolites in the culture medium that correlate with the cell growth
rate (Dietmair et al., 2012). In most cases, MVDA models have provided
support in upstream bioprocess development, process characterization,
platform development, and more recently, media development that drive
specific responses (Behrouz et al., 2020; MKS Umetrics AB, 2013; Pybus
et al., 2014; Worley & Powers, 2013). Therefore, we hypothesized that
amino acid stoichiometric balances (SB) used in combination with MVDA
models, could not only highlight key amino acids, but also provide
information on cell culture performance characteristics. While SB may
not be able to identify specific intracellular pathways or enzyme
activation due to amino acid changes, it may however serve as an
effective surrogate to extrapolate the intracellular “black box”
activities that result in the detected culture outcomes. Furthermore, we
postulate that supplementing stoichiometrically desired amino acids that
are positively correlated to cell growth or mAb productivity would help
improve the desired cell culture performance. The amino acid enrichment
‘cocktails’ can then be designed and established based on SB and MVDA
for a defined or training set of culture runs and may be used as feed
supplements for future or subsequent culture runs.
In support of our hypothesis, we trained an MVDA model using a 25-batch
study in which cells were supplemented with various amounts of a high
nutrient feed in aims to identify metabolite stoichiometric balances
predictive of cell growth or mAb productivity. Model-derived key
stoichiometric balances were translated into amino acid cocktail feeds
and supplemented at model-informed time points. In addition to the feed
media used in the training set, the model-generated amino acid cocktail
feeds were also tested as supplements for two other chemically-defined
feeds at lower nutrient concentrations, referred to as moderate and low
nutrient feeds. Moderate and low nutrient feeds contained 40% and 17%
of the carbon concentration relative to the high nutrient feed level.
Experimental validation of our platform and model-directed amino acid
supplementation showed about a 20% increase in cumulative viable cells
(CVC) and a 30% increase in mAb titer within the supplemented high
nutrient condition, about a 30% increase in CVC and 80% increase in
titer within the supplemented moderate nutrient condition, and about a
55% increase in CVC and 80% increase in titer within the supplemented
low nutrient condition. Results from our study provide evidence towards
a novel method to help improve and streamline CD media development for
bioprocess.