Accelerating bioprocess development by analysis of all available data: A USP case study

Suárez-Zuluaga, Diego A.; Borchert, Daniel; Driessen, Nicole N.; Bakker, Wilfried A.M.; Thomassen, Yvonne E.

doi:10.1016/j.vaccine.2019.07.026

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…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, cofactors, trace elements, organic, and inorganic salts among others.…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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Using MVDA with Stoichiometric Balances to Optimize Amino Acid Concentrations in Chemically Defined CHO Cell Culture Medium for Improved Culture Performance

Salim

Chauhan

Templeton

et al. 2021

Preprint

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Chemically defined (CD) media are routinely used in the production of biologics in Chinese Hamster Ovary (CHO) cell culture and provide enhanced raw material control. Nutrient optimized CD media is an important path to increase cell growth and monoclonal antibody (mAb) productivity in recombinant CHO cell lines. However, nutrient optimization efforts for CD media typically rely on multi-factorial and experimental design of experiment (DoE) approaches or complex mathematical models of cellular metabolism or gene expression systems. Moreover, the majority of these efforts are aimed at amino acids since they constitute essential nutrients in CD media as they directly contribute to biomass and protein production. In this study, we demonstrate the utilization of multi-variate data analytics (MVDA) coupled with amino acid stoichiometric balances (SBs) to increased cell growth and mAb productivity in efforts to reduce CD media development efforts. SBs measure the difference between theoretical demand of amino acids and the empirically measured fluxes to identify metabolic states of the cell. When coupled with MVDA, the statistical models were not only able to highlight key amino acids towards cell growth or productivity, but also provided direction on metabolic favorability of the amino acid. Experimental validation of our approach resulted in a 55% increase in total cell growth and about an 80% increase in total mAb productivity. Increased specific consumption of stoichiometrically balanced amino acids and decreased specific consumption of glucose was also observed in optimized CD media suggesting favorable consumption of desired nutrients and a potential for energy redistribution towards increased cellular growth or mAb productivity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using MVDA with Stoichiometric Balances to Optimize Amino Acid Concentrations in Chemically Defined CHO Cell Culture Medium for Improved Culture Performance

Salim

Chauhan

Templeton

et al. 2021

Preprint

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“…Verified relations between process parameters and cell-specific characteristics is the key knowledge to develop robust and scalable bioprocesses [ 1 ]. This knowledge is often based on regression analysis of historical data, where all possible relations are inspected [ 2 ]. Besides classical process parameters such as pH, temperature, and dissolved oxygen, scalable biomass-specific reaction rates are included in this analysis both as independent (such as product formation

) and regressor variables (such as growth µ or substrate uptake rates

) [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Kager

Herwig

2021

Bioengineering

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During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

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“…However, besides the sensor data, an increasing number of applications uses concentration data in the process analysis. [ 9,11,17,18 ] For example, MVDA was exploited for process monitoring (golden batch), [ 18,19 ] development, [ 9,11,20 ] scale‐up, [ 4 ] or reproducibility, and robustness studies together with more standard frequentist statistics. [ 17 ]…”

Section: Introductionmentioning

confidence: 99%

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

Richelle¹,

Lee²,

Portela³

et al. 2020

Biotechnology Journal

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In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale-up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell-based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed-batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale-up/-down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.

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Accelerating bioprocess development by analysis of all available data: A USP case study

Cited by 12 publications

References 26 publications

Using MVDA with Stoichiometric Balances to Optimize Amino Acid Concentrations in Chemically Defined CHO Cell Culture Medium for Improved Culture Performance

Using MVDA with Stoichiometric Balances to Optimize Amino Acid Concentrations in Chemically Defined CHO Cell Culture Medium for Improved Culture Performance

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation

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