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Record W2589472505 · doi:10.1088/0026-1394/54/1a/08007

Key comparison study on peptide purity—synthetic human C-peptide

2017· article· en· W2589472505 on OpenAlexaff
R D Josephs, Ming Li, Di Song, Steven Westwood, Norbert Stoppacher, Adeline Daireaux, Tiphaine Choteau, Robert Wielgosz, Peng Xiao, Y Liu, Xiang Gao, C Zhang, T Zhang, Wu Mi, Caixiong Quan, Ting Huang, H Li, R.B. Flatschart, Jeremy E. Melanson, Rüdiger Ohlendorf, André Henrion, T Kinumi, Lilian L. L. Wong, Q Liu, Bilgin Vatansever, İlker Ün, Ahmet C. Gören, Müslüm Akgöz, Milena Quaglia, Juanita A. Warren

Bibliographic record

VenueMetrologia · 2017
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMetrologyMutual recognitionMass fractionAmino acid analysisPeptideImpurityCharacterization (materials science)Fraction (chemistry)Standard uncertaintyChemistryChromatographyMathematicsMaterials scienceNanotechnologyStatisticsMeasurement uncertaintyAmino acidBusinessBiochemistry

Abstract

fetched live from OpenAlex

Under the auspices of the Protein Analysis Working Group (PAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) a key comparison, CCQM-K115, was coordinated by the Bureau International des Poids et Mesures (BIPM) and the Chinese National Institute of Metrology (NIM). Eight Metrology Institutes or Designated Institutes and the BIPM participated. Participants were required to assign the mass fraction of human C-peptide (hCP) present as the main component in the comparison sample for CCQM-K115. The comparison samples were prepared from synthetic human hCP purchased from a commercial supplier and used as provided without further treatment or purification. hCP was selected to be representative of the performance of a laboratory's measurement capability for the purity assignment of short (up to 5 kDa), non-cross-linked synthetic peptides/proteins. It was anticipated to provide an analytical measurement challenge representative for the value-assignment of compounds of broadly similar structural characteristics. The majority of participants used a peptide impurity corrected amino acid analysis (PICAA) approach as the amount of material that has been provided to each participant (25 mg) is insufficient to perform a full mass balance based characterization of the material by a participating laboratory. The coordinators, both the BIPM and the NIM, were the laboratories to use the mass balance approach as they had more material available. It was decided to propose KCRVs for both the hCP mass fraction and the mass fraction of the peptide related impurities as indispensable contributor regardless of the use of PICAA, mass balance or any other approach to determine the hCP purity. This allowed participants to demonstrate the efficacy of their implementation of the approaches used to determine the hCP mass fraction. In particular it allows participants to demonstrate the efficacy of their implementation of peptide related impurity identification and quantification. More detailed studies on the identification/quantification of peptide related impurities and the hydrolysis efficiency revealed that the integrity of the impurity profile of the related peptide impurities obtained by the participant is crucial for the impact on accuracy of the hCP mass fraction assignment. The assessment of the mass fraction of peptide impurities is based on the assumption that only the most exhaustive and elaborate set of results is taken for the calculation of the KCRVPepImp. The KCRVPepImp for the peptide related impurity mass fractions of the material was 83.3 mg/g with a combined standard uncertainty of 1.5 mg/g. Inspection of the degree of equivalence plots for the mass fraction of peptide impurities and additional information obtained from the peptide related impurity profile indicates that in many cases only a very small number of impurities have been identified and quantified resulting in an underestimation of the peptide related impurity mass fractions. The approach to obtain a KCRVhCP for the mass fraction of hCP is based on a mass balance calculation that takes into account the most exhaustive and elaborate set of results for the peptide related impurities KCRVPepImp, the TFA mass fraction value, water and other minor counter ions obtained by the coordinating laboratories. Differences in the quality of the results obtained for both peptides related impurity mass fractions and hCP mass fractions are better weighted and reflected in smaller uncertainties. The KCRVhCP for CCQM-K115 is 801.8 mg/g with a corresponding combined standard uncertainty of 3.1 mg/g. In general, mass balance approaches show smaller uncertainties than PICAA approaches and the majority of results obtained by the PICAA approach are in agreement because of larger corresponding uncertainties. Main text To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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)0.006
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.081
GPT teacher head0.387
Teacher spread0.305 · 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".

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Citations18
Published2017
Admission routes1
Has abstractyes

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