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Record W2023850571 · doi:10.1038/psp.2013.22

Dose Response and Pharmacokinetics of Tofacitinib (CP‐690,550), an Oral Janus Kinase Inhibitor, in the Treatment of Chronic Plaque Psoriasis

2013· article· en· W2023850571 on OpenAlexaff
Huaming Tan, Pankaj Gupta, Jane Harness, Robert Wołk, Sunny Chapel, Alan Menter, Bruce Strober, RG Langley, S. Krishnaswami, Kim Papp

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsProbity Medical ResearchDalhousie University
FundersPfizer
KeywordsTofacitinibJanus kinase inhibitorMedicinePsoriasisPharmacokineticsClinical trialPlaceboPopulationPharmacologyPlaque psoriasisInternal medicineCovariateDosingDermatologyRheumatoid arthritisStatisticsPathologyMathematics

Abstract

fetched live from OpenAlex

Longitudinal nonlinear mixed effects modeling was used to characterize the dose–response profile of tofacitinib using data from a placebo‐controlled dose‐ranging study, where tofacitinib 2, 5, and 15 mg twice daily (b.i.d.) were evaluated for plaque psoriasis treatment. Bayesian estimation was applied with prior information derived from the literature: nonclinical and clinical data in psoriasis, as well as other indications. The probability to achieve a certain target effect associated with a given dose was calculated from the posterior samples. On the basis of these probabilities along with safety considerations, tofacitinib 5 and 10 mg b.i.d. were selected for further testing in confirmatory phase III clinical trials. Pharmacokinetics in patients with psoriasis was characterized using a population‐based modeling approach, and body weight was identified as an important covariate. A subgroup analysis suggested reduced efficacy of tofacitinib with increasing body weight; however, it is unclear whether this trend could be explained by systemic exposure alone. CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e44; doi: 10.1038/psp.2013.22 ; advance online publication 22 May 2013

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)0.001
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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.309
Teacher spread0.273 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

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