A multiplexed UPLC-MS/MS assay for the simultaneous measurement of urinary safety biomarkers of drug-induced kidney injury and phospholipidosis
Bibliographic record
Abstract
Drug-induced kidney injury (DIKI) is a major concern in drug risk assessment given its clinical importance and the absence of a sensitive/specific method of diagnosis. Pharmaceutical regulatory agencies have qualified and issued letters of support for new biomarkers to better evaluate DIKI in nonclinical toxicity and clinical studies. Additional efforts have focused on drug-induced phospholipidosis (DIPL) and its potential link with collateral renal damage. The combined use of urinary biomarkers is an efficient way to evaluate renal safety in nonclinical and clinical studies. Eight FDA/EMA/PMDA qualified (or supported) urinary biomarkers, including kidney injury molecule-1 (KIM-1), β2-microglobulin (B2M), clusterin (CLU), cystatin C (CysC), trefoil factor 3 (TFF3), neutrophil gelatinase-associated lipocalin (NGAL), osteopontin (OPN), and alpha-glutathione S-transferase (α-GST), were quantified by multiplex UPLC-MS/MS in a repeat dose study of gentamicin in rats. Rats administered gentamicin at 100 mg/kg/day for 2 weeks developed renal lesions detected by histopathology. Biomarkers of tubular damage (CLU, KIM-1, OPN) increased 9.8, 34.7, and 35.6-fold (relative to concurrent controls), respectively, after 2 weeks of dosing. Biomarkers of glomerular damage and/or impairment of tubular reabsorption (CysC, B2M) increased 11.7 and 22.6-fold. NGAL and α-GST increased <3-fold after 2 weeks of dosing. TFF3 was comparable to concurrent controls. The elevated biomarker concentrations met PSTC threshold criteria and were consistent with mechanisms of gentamicin nephrotoxicity. Increased urinary di-22:6-BMP indicated concomitant DIPL as confirmed by TEM. This work provides evidence supporting the combined use of the DIKI biomarker panel and di-22:6-BMP as a biomarker of DIPL in drug risk assessment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".