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Record W4403805633 · doi:10.1177/1759720x241283536

Correlation of changes in inflammatory and collagen biomarkers with durable guselkumab efficacy through 2 years in participants with active psoriatic arthritis: results from a phase III randomized controlled trial

2024· article· en· W4403805633 on OpenAlexaff
Stefan Siebert, G. Schett, S. P. Raychaudhuri, Mónica Gumá, Warner Chen, Sheng Gao, Soumya D. Chakravarty, F. Lavie, Proton Rahman

Bibliographic record

VenueTherapeutic Advances in Musculoskeletal Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsMemorial University of Newfoundland
FundersJanssen Research and DevelopmentGenentechJanssen Scientific AffairsGilead SciencesGlaxoSmithKlineTeijin PharmaAmgenPfizerCelgeneAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicinePsoriatic arthritisBiomarkerInternal medicinePopulationArthritisPharmacodynamicsOncologyPsoriasisRandomized controlled trialImmunologyPharmacokinetics

Abstract

fetched live from OpenAlex

Background: Guselkumab (human monoclonal antibody) selectively inhibits the interleukin (IL)-23p19 subunit. Objectives: Assess the longer-term pharmacodynamic effects of guselkumab and explore associations between such effects and clinical responses in patients with active psoriatic arthritis (PsA). Design: DISCOVER-2 randomized 739 biologic-naïve patients with active PsA (swollen/tender joint counts each ⩾5, C-reactive protein (CRP) ⩾0.6 mg/dL) to guselkumab (100 mg every 4 weeks (Q4W) or at Weeks 0, 4, and then Q8W) or placebo. Guselkumab-randomized participants with available serum biomarker data (randomly selected to reflect demographic and disease characteristics of the DISCOVER-2 population) comprised inflammatory ( N = 100) and collagen ( N = 178) biomarker cohorts. Methods: Pharmacodynamic effects of guselkumab through 2 years on inflammatory and collagen biomarker levels (general linear model) and associations between biomarkers and improvements in composite measures of joint, skin, and overall disease activity (Spearman linear regression) through 2 years were assessed. The relationship between the pharmacodynamic effects of guselkumab and achieving ⩾50% improvement in the American College of Rheumatology response criteria (ACR50) was assessed using a general linear model. Results: With guselkumab, pharmacodynamic effects on inflammatory (CRP, IL-6, serum amyloid A (SAA), IL-17A, IL-17F, IL-22, and beta-defensin 2 (BD-2)) and collagen (matrix metalloproteinase-degradation type I, III, IV, and VI collagen (C1M, C3M, C4M, and C6M)) biomarker levels were sustained or enhanced through Week 100. Throughout follow-up timepoints (Week 24/52/100), decreases in CRP, IL-6, C1M, and C6M levels correlated ( r = 0.26–0.30; p < 0.05) with improved joint disease activity (Disease Activity in Psoriatic Arthritis); decreases in IL-17A, IL-17F, IL-22, and BD-2 levels correlated ( r = 0.34–0.58; p < 0.05) with improved skin disease (Psoriasis Area and Severity Index); and decreases in C1M, C3M, C4M, and C6M correlated ( r = 0.27–0.31; p < 0.05) with improved overall disease activity (Psoriatic Arthritis Disease Activity Score). Significantly ( p < 0.05) greater reductions from baseline at Week 100 in CRP, IL-6, SAA, and C1M levels were observed in participants improving from Week 24 ACR50 nonresponse to Week 100 ACR50 response and were accompanied by a significant decrease in C1M from Week 24 to Week 100 versus nonresponders at both Weeks 24 and 100. Conclusion: In biologic-naïve participants with active PsA, guselkumab elicited substantial and enduring reductions in biomarkers that were associated with durable improvements in joint, skin, and overall disease activity through 2 years of DISCOVER-2. Trial registration: NCT03158285 (clinicaltrials.gov identifier).

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)0.008
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.309
Teacher spread0.297 · 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 designRandomized trial
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
Published2024
Admission routes1
Has abstractyes

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