Conclusion
The methods presented in this study were consolidated as a single
platform approach to rapidly optimize amino acid concentrations as CD
media supplements (Fig. 8). Since amino acids are one of the most
important building blocks for cellular biomass and protein production,
they also comprise an essential component group in CD media. In
addition, since the relative amino acid requirement is similar across
CHO cell lines and similar between various mAbs, the amino acid modeling
platform presented here can be applied to various CHO cell cultures
expressing various mAbs (Széliová et al., 2020). Nevertheless, Xie and
Wang’s original stoichiometric model was inclusive of all media
nutrients and thus, our empirical modeling approach and methods to
calculate stoichiometric balances could be generalized to other
nutrients as well, for example, vitamins, co-factors, trace elements,
organic, and inorganic salts among others. Although raw measurements of
nutrient concentrations can be directly input into MVDA approaches (Fig.
8 (1a)), calculation of secondary variables such as cell growth rate,
specific productivity, and consumption rates can help formulate
stoichiometric balances which in turn can serve as enhanced inputs to
derive causal linkages (Fig. 8 (1b-1c)). Within the modeling platform,
MVDA dimensional reduction algorithms such as PLS or OPLS can be
utilized to understand the correlation between the dynamics of
time-dependent stoichiometric balances and key response variables such
as cell growth or mAb productivity. VIP statistics and correlation
coefficients from multivariate algorithms can help generate importance
and directionality criteria for the input variables. Key nutrients
towards specific response variables or cellular phenotypical states can
be further translated into experimental designs for validation studies
(Fig. 8 (2a)). However, in the case of stoichiometric balances, a
feature selection decision tree approach can be implemented to choose
the appropriate stoichiometric balances for process developing. Bolded
here is the positive correlated balances that are negative in value
which potentially represent limiting nutrients as shown in Criterion 2
(Fig. 8 (2b-2c)). However, negatively correlated stoichiometric balances
could also be considered for media formulation development. To support
multivariate statistical models, experimental validation is necessary to
not only verify the performance of the model, but also measure the
success criteria of the desired phenotypical state. Model outputs can
dictate supplementation with modified nutrient feeds to be validated in
a targeted DoE, which in turn can generate new cell culture data to
refine against the MVDA model (Fig. 8 (3 – 4)).
Since the prediction of the statistical model is largely based on the
variance of the training dataset, high variance datasets can provide a
larger prediction space for the model and a reduction in model bias.
However, models training on datasets with high variance rely heavily on
the upper and lower bounds of the variance space and thus, any
validation datasets that fall outside of the training dataset space can
lead to poor success. Therefore, as new processes are adapted based on
model-driven conditions, future datasets can be combined with training
datasets to iteratively improve the model (Fig. 8 (5)) (Luo et al.,
2020). For instance, important amino acid stoichiometric balances based
on high nutrient feeds were also fed to the moderate and low nutrient
conditions resulting in significant increases in cell growth and mAb
productivity. However, in future iterations the newly generated
validation datasets could help improve the original training dataset and
help increment towards an improved biologics production process and
optimized CD media.
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Figure Legends
Fig. 1. Variance and distribution of training dataset: A
25-batch CHO cell culture experiment was designed to obtain a training
dataset to test the amino acid stoichiometric balance MVDA model in
which the culture was supplemented with various amounts of high nutrient
feeds within a defined fed-batch process. Data on cell growth and mAb
titer from all batches was analyzed to obtain peak growth and
productivity time points and a PCA model was generated to analyze the
distribution and variance of training batches with respect to the amino
acid stoichiometric balances. (a) VCD peaked between day 6 to day 9
across the 25 training batches with a majority peaking near day 9 (Black
Lines). Similarly, CVC at each time point was also plotted along side
VCD to show a similar distribution (Green dashed lines). Cell growth
data up to day 9 represented the log-growth phase of the cell culture,
thus, day 9 CVC was chosen as the response variable for the growth
model. (b) Titer data was normalized to the peak titer value of the
fed-batch process control. Since mAb titer represented an accumulation
of extracellular secreted mAb in the spent media, peak titer occurred at
the end of the process on day 14. Accordingly, day 14 titer was chosen
as the response variable for the production model. (c). Distribution of
the batches based on principle components 1 and 2, which cumulatively
described 53.5% of the variance did not show any significant grouping
or clustering of the batches thereby eliminating the possibility of any
inherent bias in the data. (d) Similarly, the loadings plot of the
time-dependent amino acid stoichiometric balances showed a wide
distribution without any collinearity of variables at specific time
points.
Fig. 2. Criteria for Model-based Amino Acid Selection: VIP
values and coefficients from the OPLS models along with stoichiometric
balance directionalities from the process control were utilized as
selection factors in 3 unique criteria for amino acids. (a-b) Criteria 1
amino acid SBs represented positive correlation to either Day 9 CVC or
Day 14 Titer, a VIP value greater than 1, and a positive stoichiometric
balance sign. (c-d) Criteria 2 amino acid SBs represented positive
correlation to either Day 9 CVC or Day 14 Titer, a VIP value greater
than 1, and a negative stoichiometric balance sign. (e-f) Criteria 3
amino acid SBs represented positive correlation to Day 9 CVC, a VIP
value less than 0.5, and a negative stoichiometric balance sign.
Coefficient values were normalized and scaled to unit variance by the
model, and only positively correlated amino acid SBs were selected.
Since time-dependent amino acid SBs were used in the model, various
selected time points that met each respective criterion were plotted.
For each model and criteria, amino acid cocktail feeds were developed
grouped by day of importance. For instance, in the growth model for
criteria 1 (a), Asp, Ser, Pro, and Val were highlighted as important on
Day 3, and thus, a cocktail of all 4 amino acids was developed and fed
on day 3 of the culture.
Fig. 3. Criteria 1 Validation of Culture Performance with
respect too Cell Growth and mAb Productivity: Amino acids for the
growth model (Day 9 CVC) and the Production model (Day 14 Titer) were
supplemented to CHO cell culture batches that ran with the high nutrient
fed-batch process according to the selection factors from criteria 1
(Corr > 1, VIP > 1, SB > 0). Day
9 and Day 14 values for CVC and Titer were normalized to the maximum
value of the process control (black). Error bars represent standard
error to mean. (a) Amino acids supplementation for models based on
criteria 1 did not result in a significant increase in total cells by
day 9. Only the growth model showed a nominal increase of
~10% at day 14 when compared to control (p <
0.15). (b) Similarly, no significant increase of mAb productivity was
observed when compared to the process control for either model and in
the case of the growth model, mAb productivity decreased by
~20% by day 14 (p < 0.05).
Fig. 4. Criteria 2 Validation of Culture Performance with
respect too Cell Growth and mAb Productivity: In addition to the high
nutrient fed-batch process, model informed amino acid supplements based
on criteria 2 (Corr > 1, VIP > 1, SB
< 0) were also tested on moderate and low nutrient fed-batch
processes. Accordingly, Day 9 and Day 14 values for CVC and Titer were
normalized to the maximum value of each respective process control (Gray
Line). Error bars represent standard error to mean. (Relative CVC) Amino
acids supplementation based on criteria 2 showed ~20%
increase in total cells for high nutrient, but close to a
~30% increase in moderate nutrients conditions (p
< 0.05). The relative total cells was even more profound in
low nutrient reaching near ~55% increase in total cells
(p < 0.01). (Relative Titer) Antibody titer in contrast
increased only for the production model for high nutrient feeds but
drastically for both models in moderate and low nutrient feeds reaching
close to ~80% increase in titer by day 9 and a 60%
increase in titer by day 14 with respect to each process control (p
< 0.001).
Fig. 5. A comparison of total glucose consumption and specific
glucose consumption: To determine if criteria 2 amino acid
supplementation for both, the growth and the production models modified
the metabolic state of the cells, the total glucose consumption of each
culture was compared to the specific glucose consumption per cell. The
total glucose consumption was calculated by the change in extracellular
glucose concentrations between feed events in the process. Specific
glucose consumption was calculated like total glucose consumption
however, the difference was divided by the change in CVC between each
time point. Although total glucose consumption showed a slight decrease
in moderate and low nutrient conditions, a more profound decrease was
noticed in the specific glucose consumption rates for moderate and low
nutrients feeds suggesting that cells under moderate and low nutrient
conditions shifted consumption for carbon source and energy from glucose
to other preferred metabolites such as the stoichiometrically balanced
amino acids.
Fig. 6. Criteria 3 Validation of Culture Performance with
respect too Cell Growth and mAb Productivity: To test the effectiveness
of the VIP value to select biologically important amino acids only,
amino acids were supplemented according to the selection factors from
criteria 3 (Corr > 1, VIP > 0.5, SB
< 0). Supplementation based on criteria 3 was assumed to
result in a minimal change in cell growth and mAb productivity for both
models (a) Accordingly, no significant change in D14 CVC was observed
across all nutrient conditions, with some cases resulting in a decrease
of cell growth. (b) In contrast, there was a significant in increase in
titer for both moderate and low nutrient conditions in the production
model (p < 0.05), presumably due to the lack of nutrient
levels in those conditions. However, this increase was not as
significant as supplementation with criteria 2 amino acids.
Fig. 7. Driving consumption of model informed amino acids: To
validate the statistical model, it was important to assess if the model
driven amino acid supplementations led to an increased consumption of
supplemented amino acids and/or activated the consumption of other amino
acids. Specific consumption rates were calculated from the change in
extracellular amino acid levels between feed events per cell. The
resulting specific consumption rates within model conditions were
compared to the consumption rates of the respective process control for
each nutrient level. Positive changes are reflected by green boxes and
negative by red. Gray boxes indicate missing values that were not
captured by amino acid assay for various reasons. In addition, amino
acids were grouped between glucogenic and ketogenic groups.
Fig. 8. Media optimization platform with Stoichiometric
Balances: The modeling approach presented in this study was a proof of
concept to utilize stoichiometric balances for amino acids to rapidly
optimize media development efforts. However, stoichiometric balances and
the methods presented could also be applied to other macro-nutrients
necessary in CD media. Accordingly, figure 8 presents our approach as a
platform to iteratively improve various stages of media development.
Utilization of MVDA can easily highlight key stoichiometric balances
that can be filtered through a feature selection decision tree to
identify the limiting nutrients. Experimental validation studies can
then be conducted to assess the change in phenotypical behavior with the
modified media. Lastly, the newly generated data can be combined with
the original dataset to further inform and improve media development
efforts.
Supplementary Figure Text
Fig. S1. Training Model Statistics: MVDA models using the
Orthogonal Partial Least Squares (OPLS) algorithm in SIMCA-P+ were
generated using 25 training batches. Models were built towards day 9 CVC
or day 14 titer in the batch level model format. In addition to the
goodness of fit (R2) parameter, a goodness of
prediction (Q2) parameter was also generated that
measured the cross-validated prediction accuracy. (a) Observed vs
predicted plot for day 9 CVC showed a strong R2 of
0.912, and a strong Q2 of 0.726. (b). Observed vs
predicted plot for day 14 titer showed a strong R2 of
0.832 but a predictable accuracy of 0.422. Although the
Q2 was relatively lower, the day 14 titer model was
able to highlight information on key variables for media optimization.
Fig. S2. Criteria 2 Relative Growth Rate and Relative Specific
Productivity. Relative growth rate for high nutrient condition showed a
slight increased by day 3 and maintained the increase by day 7 which
could explain the slight increase (~20%) in total cells
for high nutrient feeds. Relative growth rates for moderate and low
nutrient feeds also showed only slight increases, however remained
slightly higher for a longer duration of time than the high nutrient
condition. As a result, there was a more significant increase in total
cells for moderate and low nutrient conditions. Similarly, relative
productivity (qP) showed a more profound increase in moderate and low
nutrient conditions. Moderate nutrient conditions showed a 20 increase
in qP for the growth model (p < 0.05) and showed near a 30%
increase for the production model (p < 0.01). In contrast, the
low nutrient condition showed about a 40% increase in qP starting from
day 7 (p < 0.05) suggesting the increased mAb production was
not only an artifact of increased cell growth, but a higher drive for
these cells to produce more monoclonal antibody.
Fig. S3. Cell Growth and Productivity Changes resulting from
Process Controls: Since amino acid cocktails were made at a 100x
concentration for each of the specific amino acids, increased pH by NaOH
was needed to solubilize the mixtures. In addition, highly concentrated
cocktail mixtures also resulted in a high osmolality. Therefore, to
measure the effects of high pH and high osmolality (osmo), a pH water
solution was generated at the highest pH among the amino acid cocktail
mixtures and fed to the cultures at the same volume as the cocktail
mixture. In addition, a high osmo solution in water was generated using
NaCl and fed at the same volume as the amino acid cocktail with the
highest osmo. pH controls were only measured against the high nutrient
conditions and resulted in a slight decrease of about 20% in total
cells. Similarly, pH controls showed about a 30% decrease in relative
titer. Osmo controls for high nutrient conditions showed similar trends
as the pH controls but showed minimal differences for moderate and low
nutrient conditions. The minimal overall effects of the pH control and
the osmo control on total cells and relative titer suggest that the
significant increases in cell growth and mAb production from criteria 2
cultures was attributed to the amino acids within the cocktail mixtures.