Article Type : Research Article
Authors : Fai Poon H
Keywords : Metabolomics; Critical process parameters (CPPs); Recombinant therapeutic protein titer; Chemo metrics; Canonical metabolites
As the predominated
platform of monoclonal antibodies (mAbs) production, it is critical to
understand the biological events occur in CHO cells during the manufacturing
process. These biological events are often referred as a black box in the industry,
and are focusd by many studies due to the recent quality by design (QbD)
initiative launched by many regulatory bodies such as FDA and EMA. The QbD
efforts are used to understand critical process parameters (CPPs) relevant to
its productivity and quality. Many omics studies are used to shine light into
this biological black box of CHO cells. However, little study has been done to
investigate the biological changes during fermentation by changing of CPPs.
These studies are difficult due to large amount of samples and a big set of
data. In order to overcome these obstacles, we proposed a dsign of experiment
(DoE) to reduce the number of experiments in bioreactor studies to investigate
the effect of process parameters (pH, temperature shift and dissolve oxygen
(DO)) on protein titer. In this proposed study, pH, temperature shift or DO
will be determine if they are CPPs that affect protein titer, and various
metabolomic profiles in the bioreactors were also studied. The generated data
were analyzed by multivariate data analysis (MVDA) in order to identify the
metabolites that were altered by the change of CPPs. The change of DO, pH and
temperature in the bioreactor environment lead to significant alternation of
metabolites. Therefore, we can speculate the changes of these metabolites lead
to titer improvement.
Chinese hamster ovary (CHO) cells are one of the most preferred hosts for industrial production of recombinant therapeutic proteins [1]. The performance of CHO cells is usually affected by the extracellular environment that is determined by the process parameters [2]. It is well established that changing of process parameters, such as temperature shift, gas flow rate, dissolve oxygen (DO) and pH, could alter the metabolism of the cells and improve the productivity and quality of monoclonal antibodies (mAbs) [3-5]. Therefore, identifying the key process parameters in manufacturing process is critical to obtain consistent product titer and quality [6]. It also facilitates the understanding of the relationship between the critical process parameters (CPPs) and process output (i.e. productivity, quality etc), which is the key to quality by design (QbD) initiatives by regulatory agencies. Numerous studies are focusing on the bioprocess outcomes, such as viable cell density (VCD), protein productivity and glycosylation [7-9]. However, biological events that are associated with the changes of CPPs have not been wildly investigated [10]. Advance of system biology, such as genomics, proteomics and metabolomics, enables data-driven studies of biological events in a system that is often unclear [11]. Therefore, it provides a solution to understand the biological black box of bioprocess. However, these omics studies of bioprocess are difficult to achieve due to large amount of samples and data generation. To overcome these difficulties, design of experiments (DoE) is needed to limit the samples in the study; and multivariate data analysis (MVDA) is needed to analyze the generated big data set. Therefore, in order to gain insights into the biological events during bioprocess parameters optimization, we propose metabolomics to investigate the biological events during DoE identification of CPPs that affect titer. These data can then analyzed by MVDA to reveal the canonical biological pathways. DoE can significantly reduce the number of experiments required to identify important CPPs while retaining maximum certainty in the effects of the experimental parameters [12]. For bioprocess development, DoE has been successfully applied for identification and optimization of CPPs which are critical for improving cell growth and controlling specific cellular metabolic activity [13-15]. Metabolomics is a common omics method to profile essential nutrients and by-products produced by host cells during bioprocess, and therefore provides insight into the metabolism and canonical pathways. Combining with MVDA, it holds promise to uncover the metabolic characteristics of the optimized process that are critical for both CPPs and titer.
The proposed experimental workflow was summarized in (Figure 1). Briefly, DoE can be set up to identify the CPPs. The metabolic profile of these experiments was determined by liquid chromatography with mass spectrometer (LC-MS). Process parameters that significantly affect the protein titer will be identified the common metabolites identified will be considered to be relevant to both CCPs and titer change, indicating that these metabolites playing critical roles in the improvement of titer when CPPs were altered.
Figure 1: Workflow overview of current study to identify the metabolites associated with CPPs change and protein titer.
The analyzed
metabolites should be focused on amino acids, intermediate products
of tricarboxylic acid cycle (TCA), nucleosides and carbon source.
The different
process parameters (pH, DO and temperature shift) as X and titer as Y, can be modeled
by PLS to identify CCPs. The value R2Y represented the extracted principal component could
sufficiently explain the alteration in Y. The value of Q2 can show a sufficient predictability of the model.
The change of parameters that shows
statistical significance of alteration
in the process outcomes, will be identified as CPPs for titer.
The DoE of process
parameters will enable us to observe the alteration of process outcome, and the metabolism and biological events
in CHO cells associating with these changes. PCA model can reflect
the change of “black box” biological events from the exponential phase to stationary phase, and eventually to apoptotic phase. Within the population of each time
point, subpopulations can be identified. For
example, samples can be grouped
into subpopulations by their pH set values.
The subpopulations will indicated
that there is difference between the metabolic
profile of high titer batches
and low titer batches.
Since pH may be identified as one of the CPPs that affect titer value at different bioreactor runs, we can further
research on the subpopulation in the score plot by different levels of pH
values. The samples of bioreactor runs with different pH will be located on different part of the score
plot. Separation of subpopulations should be
particularly clear in the stationary phase. The score contribution plot, showing which metabolites were influential for the
chang of CPP, can be created to
identify the metabolites markedly
impacted by different levels of pH in different culture phases. For example, metabolites that are involved in
transmembrane transport and cell metabolism (such as TCA cycles) can be identified when pH of the culture changed,
thus one can speculate that when CHO cells were cultured at different pH, it enabled better transmembrane transport to allow larger
amount of amino acids to enter the cells for protein synthesis.
The increased consumption of amino acids was previously reported [16].
Temperature
shift should be more beneficial for recombinant
therapeutic protein production since previous report by Bollati-Fogolín showed that a temperature shift from a 37oC to 35oC during stationary phase could increase
titer while maintaining high cell density [17]. It was
well documented that the temperature
shift was routinely used to alter the output of CHO cells cultivated in bioreactors. However, the few researches have been done to investigate the biological events
during the temperature shifted. Therefore, a number of potential metabolites will be altered by the 35oC temperature shift were presented to gain insight into these black box biological events. These
metabolites would aid to understand the
connection between culture temperature shift and recombinant therapeutic protein productivity.
The connection between
CPPs and recombinant therapeutic protein yield remains as a black-box in bioprocess. The exploration of metabolites can help to gain insights
into the potential mechanisms of how CPPs affect protein
production. In the above two sections, we can identify
series of metabolites impacted by changes
of culture pH values and temperature. In this part,
the metabolites will show a statistically
significant association with protein titer by Pearson’s
correlation test in different cell phases (Figure 1). Pearson’s
correlation test will be used to determine the correlation between the concentrations of metabolites and the protein
titer values in the bioreactors. The correlation with P <
0.05 will be considered as statistically significant.
The association between aspartate [16] and protein titer was expected since previous
study reported that high concentration of aspartate in medium would inhibit the expression of protein [18].
Nucleosides are the main materials for
cellular gene expression. Therefore, gene expression related
matabiolite might also involve in the titer improvement during temperature shift. The change of nucleosides in this proposed study will be consistent with previous report
by Richardson et al.
[19]. Similar to nucleosides, amino
acids should also be
identified to have a statistical significant correlation with protein titer value. Together
with the nucleosides data, we speculated that protein metabolism and gene expression in CHO cells can be activated
by the CPPs changes in the proposed
study to achieve higher titer in CHO cells. Another
canonical pathways can potentially identified by the metabolic study were TCA cycles in cell metabolism. A
negative correlation of glutamine
attributed to the rapid consumption should be expected as Hong’s
work did [20]. Malate was involved in TCA cycle for supplying energy during cell culture process.
Chong et al also found
that malate accumulation throughout the culture
process [21]. Lactate was one of the main waste
products during cell culture (Zhou et al. 2011), which should be also accumulated in our proposed study. Therefore, it was speculative that TCA cycles
and its metabolites played key roles in CPPs inducing titer change.
Combining with the results of PCA, the metabolites linking with pH and temperature shift to recombinant therapeutic protein titer will be all identified the corresponding metabolic pathways of these metabolites are valuable. The biological events linking
with the CPPs to protein
titer can be identified and illustrated using Reactome Pathway Software. This in silico analysis can confirm the metabolic
pathways that are sensitive
to pH changes. Temperature changes
altered the responses of other pathways.
Therefore, we speculated
that the change of pH and the temperature shift induced the
variation of transmembrane transport and metabolism.
These changes lead to variation in titer.
By using the state-of-the-art technologies and
methods to analyze the CHO cell metabolome during culture, we will be able to
gain insight into the biological characteristics during CPPs induced changes in titer. In this proposed work, PLS will be used to identify the CPPs from DoE,
PCA and Pearson’s correlation test will
be applied to interpret the metabolic data to gain valuable information. A
series of metabolic makers influenced
by pH and temperature shift can be successfully
identified. We will find the metabolites that are associated with
relevant cell metabolism were key to the titer change when pH change and temperature shift occurred. This facilitated the understanding of the
biological events during production
process under different conditions and increasing of cell culture knowledge base.