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Record W4220845977 · doi:10.1002/jlcr.3969

Synthesis of [<sup>2</sup>H<sub>5</sub>]baricitinib via [<sup>2</sup>H<sub>5</sub>]ethanesulfonyl chloride

2022· article· en· W4220845977 on OpenAlexafffund
Ross D. Jansen‐van Vuuren, Rahul Vohra

Bibliographic record

VenueJournal of Labelled Compounds and Radiopharmaceuticals · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsDefence Research and Development CanadaQueen's University
FundersQueen's University
KeywordsChemistryYield (engineering)DeuteriumIsotopeChlorideCombinatorial chemistryRadiochemistryOrganic chemistryThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

Baricitinib, typically applied as a treatment for rheumatoid arthritis, has recently attracted the attention of clinicians and researchers as a potential treatment for COVID‐19. Naturally, there has been a need for the preparation of the isotope‐labelled analogue of baricitinib to probe the pharmacokinetics of baricitinib in this new role. As such, we have developed a simple synthetic route to deuterated [ 2 H 5 ]baricitinib, facilitating its formation over four steps and in a 29% overall yield based on starting [ 2 H 5 ]ethanethiol (19% if we start with [ 2 H 5 ]bromoethane instead). A critical component of the overall process involves the synthesis of [ 2 H 5 ]ethanesulfonyl chloride, and we describe in detail the two routes that were explored to optimize this step.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.128
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
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 head0.049
GPT teacher head0.351
Teacher spread0.302 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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