Disease-Modifying Therapies in Lupus Nephritis: A Narrative Review Evaluating Currently Used Pharmacologic Agents
Bibliographic record
Abstract
As more lupus nephritis (LN) medications become available, identifying treatments that are disease-modifying is critical in making treatment decisions. Based on our 2022 published working definition of LN disease modification as 'minimizing disease activity with the fewest treatment-associated toxicities and slowing progression to end-stage kidney disease' (ESKD), the objective of this review was to classify current LN treatments according to the proposed kidney disease modification criteria, excluding toxicities. Based upon a selection of LN clinical trial (n = 27) and observational study (n = 20) publications, as well as the authors' clinical experiences, we evaluated the disease modification potential for 16 LN treatments (inclusive of antimalarials, glucocorticoids, immunosuppressants, calcineurin inhibitors and biologics) according to the proposed kidney disease activity and organ damage criteria at year 1, years 2-5, and > 5-year time points. Fulfilling criteria at year 1 and years 2-5 was considered evidence for disease modification potential. Satisfying criteria at > 5 years (slowing or preventing progression in SLICC/ACR Damage Index [SDI] and ESKD, and/or doubling of serum creatinine) was used to confirm disease modification. Each treatment was designated as one of the following at each time point: (a) criterion met; (b) inconclusive; (c) no available supportive data. This review excluded an assessment of potential toxicities. All LN treatments met at least one of the potential kidney disease-modification criteria at any time point, but limited relevant data in the literature meant disease modification > 5 years could only be confirmed for cyclophosphamide. Belimumab met more criteria across the three time points than any other biologic treatment but lacked > 5-year data to confirm disease modification. Further research is needed to support the classification of LN treatments as disease modifiers, particularly for > 5 years. We discuss considerations for future studies, challenges to the classification, and possible updates to published criteria.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".