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Record W2586915005 · doi:10.1093/ndt/gfw178.43

SP684A CALCINEURIN INHIBITOR WITH AN IMPROVED SIDE EFFECT PROFILE?

2016· article· en· W2586915005 on OpenAlexaff
Robert B. Huizinga, Neil Solomons, Mark D. Abel

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsAurinia (Canada)
Fundersnot available
KeywordsMedicineCalcineurinPharmacologyInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Introduction and Aims: The current generic calcineurin inhibitors (CNIs) cyclosporine A and tacrolimus (TAC) have demonstrated success in multi-target therapy in lupus nephritis (LN) but are associated with many efficacy-limiting comorbidities. CNIs stabilize podocytes and protect against proteinuria via a dual mechanism involving calcineurin inhibition and remain valuable in treating lupus nephritis. However, CNI-related adverse effects include hypertension, renal toxicity and diabetes. Voclosporin (VCS) is a novel CNI intended for use in the treatment of autoimmune diseases such as lupus nephritis. VCS was created by adding a single carbon extension to the amino acid-1 region of cyclosporine A. This alters the cyclophilin-voclosporin complex binding to calcineurin, increasing the potency of voclosporin and shifting the primary site for voclosporin metabolism resulting in less competitive antagonism. The results of these changes allows for the administration of lower doses, less pharmacokinetic-pharmacodynamic variability and a potentially improved safety profile compared with other CNIs. As TAC and VCS inhibit calcineurin and prevent NFATc activation through different mechanisms, a Phase 2b renal transplant study (PROMISE) confirmed that the incidence of diabetes was significantly lower at 6 months post-transplant in a low-VCS treatment group compared to a TAC treatment group (1.6% vs. 16.4%, respectively, p = 0.031).

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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