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Understanding factors that limit the productivity of suspension-based perfusion cultures operated at high medium renewal rates

2000· article· en· W1973972095 on OpenAlexaff
Sylvain Mercille, Mark W. Johnson, Stéphane Lanthier, Amine Kamen, Bernard Massie

Bibliographic record

VenueBiotechnology and Bioengineering · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversité de MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSpargingBioreactorGlutaminePerfusionChemistryAir spargingChemostatChromatographyBiochemistryAnimal scienceBiologyInternal medicineContaminationEcology

Abstract

fetched live from OpenAlex

One of the key parameters in perfusion culture is the rate of medium replacement (D). Intensifying D results in enhanced provision of nutrients, which can lead to an increase in the viable cell density (X(v)). The daily MAb production of hybridoma cells can thus be increased proportionally without modifying the bioreactor scale, provided that both viable cell yield per perfusion rate (Y(Xv/D)) and specific MAb productivity (q(MAb)) remain constant at higher D. To identify factors prone to limit productivity in perfusion, a detailed kinetic analysis was carried out on a series of cultures operated within a D range of 0.48/4.34 vvd (volumes of medium/reactor volume/day) in two different suspension-based systems. In the Celligen/vortex-flow filter system, significant reductions in Y(Xv/D) and q(MAb) resulting from the use of gas sparging were observed at D > 1.57 vvd (X(v) > 15 x 10(6) cells/mL). Through glucose supplementation, we have shown that the decrease in Y(Xv/D) encountered in presence of sparging was not resulting from increased cellular destruction or reduced cell growth, but rather from glucose limitation. Thus, increases in hydrodynamic shear stress imparted to the culture via intensification of gas sparging resulted in a gradual increase in specific glucose consumption (q(glc)) and lactate production rates (q(lac)), while no variations were observed in glutamine-consumption rates. As a result, while glutamine was the sole limiting-nutrient under non-sparging conditions, both glutamine and glucose became limiting under sparging conditions. Although a reduction in q(MAb) was observed at high-sparging rates, inhibition of MAb synthesis did not result from direct impact of bubbles, but was rather associated with elevated lactate levels (25-30 mM), resulting from shear stress-induced increases in q(lac), q(glc), and Y(lac/glc). Deleterious effects of sparging on Y(Xv/D) and q(MAb) encountered in the Celligen/vortex-flow filter system were eliminated in the sparging-free low-shear environment of the Chemap-HRI/ultrasonic filter system, allowing for the maintenance of up to 37 x 10(6) viable cells/mL. A strategy aimed at reducing requirements for sparging in large-scale perfusion cultures by way of a reduction in the oxygen demand using cellular engineering is discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.235
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2000
Admission routes1
Has abstractyes

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