SP684A CALCINEURIN INHIBITOR WITH AN IMPROVED SIDE EFFECT PROFILE?
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".