Sirukumab: A Potential Treatment for Mood Disorders?
Bibliographic record
Abstract
Convergent evidence indicates that abnormalities in the innate immune system may be pertinent to the pathogenesis, phenomenology, and possible treatment of several mental disorders. In keeping with this view, the targeting of interleukin-6 with the human monoclonal antibody sirukumab may represent a possible treatment and disease modification approach, for adults with brain-based disorders (e.g., major depressive disorder). A PubMed/Medline database search was performed using the following search terms: sirukumab; anti-IL-6; IL-6; major depressive disorder; inflammation. A systematic review was conducted of both preclinical and clinical trials reporting on the pharmacology of sirukumab or investigating the efficacy of targeting IL-6 signaling. Overall, sirukumab has been reported to be a safe and well-tolerated agent, capable of modulating the immune response in healthy populations as well as in subjects with inflammatory disorders (e.g., rheumatoid arthritis). Sirukumab's effects on cytokine networks as part of the innate immune system provide a coherent rationale for possible application in neuropsychiatric disorders with possible benefits across several domains of the biobehavioral Research Domain Criteria matrix (e.g., general cognitive processes, positive valence systems). Amongst individuals with complex brain-based disorders (e.g., mood disorders), the dimensions/domains most likely to benefit with sirukumab are negative valence disturbances (e.g., anxiety, depression, rumination), positive valence disturbances (e.g., anhedonia) as well as general cognitive processes. We suggest that sirukumab represents a prototype and possibly a proof-of-concept that agents that engage IL-6 targets have salutary effects in psychiatry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".