A comprehensive approach to identify approved drugs and treatments for repositioning as therapies for systemic lupus erythematosus
Bibliographic record
Abstract
Development of new systemic lupus erythematosus (SLE) treatments has been slow. To accelerate the pace, an evidence-based approach was developed to find new lupus therapies amongst 6,800 compounds FDA approved for human use. The Lupus Treatment List (LRxL) was constructed with intense input from the entire lupus community, including patients, and was used to prioritize therapies to be tested in small focused, biomarker-rich clinical trials (SLE Treatment Acceleration Trials (STAT)). All drugs widely used for lupus or known to be in development for lupus by Pharma/Biotech were excluded. Details of the project can be viewed online [ 1 ]. A novel evidence-based composite scoring system was developed to rank the identified drugs/therapies numerically by scientific rationale, experience in lupus mice/human cells, previous clinical experience in autoimmunity, drug properties and adverse event profile. Of the 157 therapies initially screened, more than 25 have an appropriate set of characteristics to consider for testing in clinical trials in lupus, including drugs targeting cellular metabolism, kinases, the immune system, HDACs, complement as well as cellular therapies and nondrug interventions. This approach has not only identified unique candidates that could be useful in SLE and possibly other autoimmune/inflammatory conditions, but has also yielded a rigorous evidence-based process by which therapies can be usefully rated for possible clinical application to treat these conditions, thereby mitigating risk in drug development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".