Use of Pharmacogenetics, Enzymatic Phenotyping, and Metabolite Monitoring to Guide Treatment with Azathioprine in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Individualized therapy based on genetic background and monitoring of metabolites can optimize drug safety and efficacy. Such an approach is available for azathioprine (AZA), the thiopurine antimetabolite. AZA exerts therapeutic effects when metabolized to the active thiopurine nucleotide, 6-thioguanine (6-TGN). In inflammatory bowel disease (IBD), 6-TGN levels in the target range of 235-400 pmol/8x10(8) red blood cells (RBC) are associated with a high likelihood of response. Our objective was to evaluate whether drug escalation based on metabolite levels improves efficacy and maintains safety in patients with systemic lupus erythematosus (SLE). METHODS: We conducted a 6-month open-label dose-escalation clinical study of patients with active SLE treated with azathioprine dosed by body weight and metabolite levels. The primary endpoint was >or=50% improvement in any one parameter of disease activity, or 50% decrease in glucocorticoid dose. RESULTS: Of 50 patients enrolled in the study, 21 achieved clinical responses, 13 of whom had 6-TGN<235 pmol/8 x10(8) RBC. Ten patients had no clinical response at 6 months, yet achieved either therapeutic IBD 6-TGN levels (>235, n=4) or received maximum AZA dose>or=3.5 mg/kg (n=6). In 19 patients the drug was discontinued prematurely due to side effects or SLE activity. For those patients in whom either liver function test or white blood cell count abnormalities were encountered, metabolites guided attribution to drug or disease activity. CONCLUSION: Clinical responses in SLE can occur at levels of 6-TGN lower than the target range established for IBD. During followup, measurements of AZA metabolites may provide a rational approach to safety.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".