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Record W4366463978 · doi:10.1002/elps.202200284

An interlaboratory capillary zone electrophoresis‐UV study of various monoclonal antibodies, instruments, and ε‐aminocaproic acid lots

2023· article· en· W4366463978 on OpenAlexaff
Rebecca Wiesner, Holger Zagst, Wenkui Lan, Stewart Bigelow, Peter Holper, Göran Hübner, Leila Josefsson, Claire M. Ouimet, Lili Lo, Christopher Lößner, Huixin Lu, Christian Neusüß, Carolin Rüttiger, Johannes Schlecht, Philipp Schürrle, Alexander Selsam, Debbie van der Burg, Shao‐Chun Wang, Yunxiao Zhu, Hermann Wätzig, Cari Sänger van de Griend

Bibliographic record

VenueElectrophoresis · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsHealth Canada
Fundersnot available
KeywordsCapillary electrophoresisChromatographyChemistryResolution (logic)Analytical Chemistry (journal)Computer science

Abstract

fetched live from OpenAlex

Capillary zone electrophoresis ultraviolet (CZE-UV) has become increasingly popular for the charge heterogeneity determination of mAbs and vaccines. The ε-aminocaproic acid (eACA) CZE-UV method has been used as a rapid platform method. However, in the last years, several issues have been observed, for example, loss in electrophoretic resolution or baseline drifts. Evaluating the role of eACA on the reported issues, various laboratories were requested to provide their routinely used eACA CZE-UV methods, and background electrolyte compositions. Although every laboratory claimed to use the He et al. eACA CZE-UV method, most methods actually deviate from He's. Subsequently, a detailed interlaboratory study was designed wherein two commercially available mAbs (Waters' Mass Check Standard mAb [pI 7] and NISTmAb [pI 9]) were provided to each laboratory, along with two detailed eACA CZE-UV protocols for a short-end, high-speed, and a long-end, high-resolution method. Ten laboratories participated each using their own instruments, and commodities, showing excellence method performance (relative standard deviations [RSDs] of percent time-corrected main peak areas from 0.2% to 1.9%, and RSDs of migration times from 0.7% to 1.8% [n = 50 per laboratory], analysis times in some cases as short as 2.5 min). This study clarified that eACA is not the main reason for the abovementioned variations.

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 categoriesMeta-epidemiology (narrow)
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 score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.215
Teacher spread0.210 · 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.

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

Citations17
Published2023
Admission routes1
Has abstractyes

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