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Record W2793455098 · doi:10.1093/ecco-jcc/jjx180.614

P487 Tofacitinib for the treatment of ulcerative colitis: Analysis of infection rates from the OCTAVE clinical programme

2018· article· en· W2793455098 on OpenAlexaff
Kevin Winthrop, Edward V. Loftus, Daniel C. Baumgart, Walter Reinisch, Andrew Thorpe, Chudy I. Nduaka, Nervin Lawendy, Gary Chan, Ron Pedersen, Gary S. Friedman, Chunyan Su

Bibliographic record

VenueJournal of Crohn s and Colitis · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTofacitinibMedicineUlcerative colitisInternal medicineJanus kinase inhibitorCohortIncidence (geometry)GastroenterologyRheumatoid arthritisDisease

Abstract

fetched live from OpenAlex

Tofacitinib is an oral, small molecule Janus kinase inhibitor that is being investigated for ulcerative colitis (UC). The safety of tofacitinib for treatment of moderate to severe UC was evaluated in 8-week Induction Phase (P) 2 (NCT00787202), 8-week Induction P3 (NCT01465763; NCT01458951) and 52-week Maintenance P3 (NCT01458574) studies,1 as well as an ongoing, open-label, long-term extension (LTE) study (OCTAVE Open, NCT01470612).2 Here, we present analysis of infections observed during the UC clinical development programme. Patients who received placebo, tofacitinib 5 or 10 mg twice daily (BID) were analysed as three cohorts: Induction (P2 and P3 studies, N = 1220); maintenance (P3 study, N = 592); and overall (patients in P2, P3 and ongoing LTE studies receiving tofacitinib 5 or 10 mg BID, N = 1157). Data are shown as of 16 December 2016. Proportions and incidence rates (IRs; patients with events per 100 patient-years [PY] of exposure, 95% CI) were evaluated for infections of special interest. Opportunistic infections (OIs) were based on review by an independent adjudication committee. In total, 1157 patients received ≥1 dose of tofacitinib 5 or 10 mg BID with 1613 PY of tofacitinib exposure (median 514 days) and ≤4.4 years of treatment. Demographics were generally similar across all treatment groups (Table). The most frequently occurring infection in all cohorts was nasopharyngitis. The serious infection events (SIEs) IR (95% CI) in the overall cohort, 1.99 (1.37, 2.79), was similar to the IRs in the maintenance cohort, 1.35 (0.16, 4.87) for 5 mg BID and 0.64 (0.02, 3.54) for 10 mg BID, suggesting that the risk of SIEs did not increase with duration of tofacitinib treatment. There was no apparent clustering of specific types of SIEs, nor apparent dose dependency in the risk of SIEs. OIs were reported in 21 patients, with an IR in the overall cohort of 1.28 (0.79, 1.96). Most OIs were herpes zoster (HZ) (17 patients, IR 1.04 [0.60, 1.66]), which was non-serious and mostly limited to skin involvement SIEs were infrequent in the UC programme, with no apparent clustering of specific types of SIEs nor dose dependency in the risk of SIEs. OIs occurred infrequently, HZ being the most frequent, with no evidence for an increasing risk of OI with tofacitinib treatment duration. The safety profile generally appeared similar to that previously reported in rheumatoid arthritis (including increased risk of HZ)3 and that of other UC therapies including biologics. Abstract P487 1. Sandborn WJ et al. Tofacitinib as induction and maintenance therapy for ulcerative colitis. N Engl J Med, 2017;376(18):1723–1736. 2. Lichtenstein GR et al. Tofacitinib, an oral janus kinase inhibitor, in the treatment of ulcerative colitis: open-label, long-term extension study. Am J Gastroenterol, 2017;112(S1): Abstract 714. 3. Cohen SB et al. Long-term safety of tofacitinib for the treatment of rheumatoid arthritis up to 8.5 years: integrated analysis of data from the global clinical trials. Ann Rheum Dis, 2017;76(7):1253–1262.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.331
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 designMeta-analysis
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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Citations0
Published2018
Admission routes1
Has abstractyes

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