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Record W3151883212 · doi:10.1016/j.csbj.2021.03.030

In-line monitoring of surfactant clearance in viral vaccine downstream processing

2021· article· en· W3151883212 on OpenAlexaff
Jessie Payne, James T. Cronin, Manjit Haer, Jason Krouse, William Prosperi, Katherine Drolet-Vives, Matthew Lieve, Michael Soika, Matthew T. Balmer, Marina Kirkitadze

Bibliographic record

VenueComputational and Structural Biotechnology Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsSanofi (Canada)Queen's University
Fundersnot available
KeywordsPulmonary surfactantUnivariateCross-flow filtrationFiltration (mathematics)ChromatographyMultivariate statisticsMaterials scienceDownstream processingChemistryMembraneMathematicsStatistics

Abstract

fetched live from OpenAlex

PURPOSE: The goal of this study is to examine the suitability of in-line infrared measurements to monitor, in real-time, surfactant concentration in the viral vaccine drug substance during a 50KDa tangential flow filtration (TFF) process. METHODS: A ReactIR™ 702L instrument was used to gather spectra of process off-line samples and reference materials to assess the feasibility of monitoring surfactant concentration during a TFF process in real-time. Both univariate and multivariate models were used to evaluate the off-line sample data and were found to be in good agreement with surfactant concentration values obtained by HPLC. These results were used as justification for a real-time TFF experiment with live process material. RESULTS: spectral region can be used to predict surfactant concentrations between TFF exchanges 8 to 15. CONCLUSION: The results of this study demonstrated suitability of an in-line infrared measurement to monitor surfactant concentration in the viral vaccine drug substance between exchanges 8-15 of a 50 kDa tangential flow filtration process. The preliminary multivariate model used for this work can be further optimized for the in-line use at manufacturing scale.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.266
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2021
Admission routes1
Has abstractyes

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