Avasopasem manganese (GC4419) protects against cisplatin-induced chronic kidney disease: An exploratory analysis of renal metrics from a randomized phase 2b clinical trial in head and neck cancer patients
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) patients treated with high-dose cisplatin concurrently with radiotherapy (hdCis-RT) commonly suffer kidney injury leading to acute and chronic kidney disease (AKD and CKD, respectively). We conducted a retrospective analysis of renal function and kidney injury-related plasma biomarkers in a subset of HNSCC subjects receiving hdCis-RT in a double-blinded, placebo-controlled clinical trial (NCT02508389) evaluating the superoxide dismutase mimetic, avasopasem manganese (AVA), an investigational new drug. We found that 90 mg AVA treatment prevented a significant reduction in estimated glomerular filtration rate (eGFR) three months as well as six and twelve months after treatment compared to 30 mg AVA and placebo. Moreover, AVA treatment may have allowed renal repair in the first 22 days following cisplatin treatment as evidenced by an increase in epithelial growth factor (EGF), known to aid in renal recovery. An upward trend was also observed in plasma iron homeostasis proteins including total iron (Fe-blood) and iron saturation (Fe-saturation) in the 90 mg AVA group versus placebo. These data support the hypothesis that treatment with 90 mg AVA mitigates cisplatin-induced CKD by inhibiting hdCis-induced renal changes and promoting renal recovery.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".