Current and future therapies for SLE: obstacles and recommendations for the development of novel treatments
Bibliographic record
Abstract
SLE is a serious, debilitating autoimmune disease that affects various organs and body systems. Of all the heterogeneous autoimmune diseases, SLE is perhaps the most heterogeneous. Patients with SLE, who are primarily female, have diverse disease manifestations and severity. SLE is characterised by substantial concentrations of autoantibodies against nuclear antigens, which are thought to be caused by immune cell dysregulation. Until recently, several immunosuppressant agents were used to treat this disease. Efforts to develop drugs against targets potentially involved in disease mechanisms have resulted in the identification and use of BAFF (B-cell activating factor)/APRIL (a proliferation-inducing ligand) inhibitors to treat SLE. Drugs in late-stage development that focus on pathways that are dysregulated in SLE include those that target the interferon pathway, T-cell signalling and B-cell signalling. New therapeutic agents are still necessary because of the unmet medical needs associated with this disease, including insufficient disease control, poor health-related quality of life, comorbidities, toxicity of the majority of therapies and diminished survival. Despite the substantial long-term investment of research, clinical activity and resources for identifying new treatments for this disease, only one new therapy, the biological belimumab, has been approved in the past 50 years. Efforts to develop drugs to address these needs are challenged by problems associated with disease heterogeneity, variable disease mechanisms and trial design. This review provides an overview of current and future treatments, discusses challenges in the SLE drug development process and offers recommendations for overcoming these challenges.
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".