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Record W3116975117 · doi:10.1681/asn.2020071106

A Randomized Clinical Trial of Anti–IL-6 Antibody Clazakizumab in Late Antibody-Mediated Kidney Transplant Rejection

2020· article· en· W3116975117 on OpenAlexaff
Konstantin Doberer, Michael Duerr, Philip F. Halloran, Farsad Eskandary, Klemens Budde, Heinz Regele, J. Reeve, Anita Borski, Nicolas Kozakowski, Roman Reindl‐Schwaighofer, Johannes Waiser, Nils Lachmann, Sabine Schranz, Christa Firbas, Jakob Mühlbacher, Georg Gelbenegger, Thomas Perkmann, Markus Wahrmann, Alexander Kainz, Robin Ristl, Fabian Halleck, Gregor Bond, Edward Chong, Bernd Jilma, Georg A. Böhmig

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineAntibodyKidney transplantationDonor specific antibodiesImmunologyKidney transplantRandomized controlled trialGraft rejectionKidneyInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Significance Statement There is no proven effective treatment for a major cause of graft failure, late antibody-mediated rejection, but IL-6, a cytokine known to promote B cell immunity, may be a promising therapeutic target. The authors describe the results of a phase 2 randomized clinical trial involving 20 patients, designed to evaluate the safety (primary endpoint) and efficacy (secondary endpoint analysis) of an anti–IL-6 antibody, clazakizumab, versus placebo in late antibody-mediated rejection. Although the occurrence of serious infections and diverticulitis presented important safety signals, clazakizumab was associated with an early decrease in donor-specific antibody levels, modulated antibody-mediated rejection activity, and slowed the decline of renal function. Preliminary efficacy results suggest a potentially beneficial effect of clazakizumab and may therefore support the design of larger trials with a longer duration of follow-up. Background Late antibody-mediated rejection (ABMR) is a leading cause of transplant failure. Blocking IL-6 has been proposed as a promising therapeutic strategy. Methods We performed a phase 2 randomized pilot trial to evaluate the safety (primary endpoint) and efficacy (secondary endpoint analysis) of the anti–IL-6 antibody clazakizumab in late ABMR. The trial included 20 kidney transplant recipients with donor-specific, antibody-positive ABMR ≥365 days post-transplantation. Patients were randomized 1:1 to receive 25 mg clazakizumab or placebo (4-weekly subcutaneous injections) for 12 weeks (part A), followed by a 40-week open-label extension (part B), during which time all participants received clazakizumab. Results Five (25%) patients under active treatment developed serious infectious events, and two (10%) developed diverticular disease complications, leading to trial withdrawal. Those receiving clazakizumab displayed significantly decreased donor-specific antibodies and, on prolonged treatment, modulated rejection-related gene-expression patterns. In 18 patients, allograft biopsies after 51 weeks revealed a negative molecular ABMR score in seven (38.9%), disappearance of capillary C4d deposits in five (27.8%), and resolution of morphologic ABMR activity in four (22.2%). Although proteinuria remained stable, the mean eGFR decline during part A was slower with clazakizumab compared with placebo (−0.96; 95% confidence interval [95% CI], −1.96 to 0.03 versus −2.43; 95% CI, −3.40 to −1.46 ml/min per 1.73 m 2 per month, respectively, P =0.04). During part B, the slope of eGFR decline for patients who were switched from placebo to clazakizumab improved and no longer differed significantly from patients initially allocated to clazakizumab. Conclusions Although safety data indicate the need for careful patient selection and monitoring, our preliminary efficacy results suggest a potentially beneficial effect of clazakizumab on ABMR activity and progression.

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 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 score0.125
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.368
Teacher spread0.331 · 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.

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

Citations175
Published2020
Admission routes1
Has abstractyes

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