Exposure-response of serum biomarkers to vamorolone, a dissociative corticosteroidal anti-inflammatory drug, in 4- to <7-year children
Bibliographic record
Abstract
OBJECTIVES: Corticosteroid agonists of the glucocorticoid receptor are a mainstay of therapeutics for pro-inflammatory conditions. Vamorolone is a novel partial agonist that is differentiated from the other members of the corticosteroid class by non-metabolism by 11β-hydroxysteroid dehydrogenases, antagonism of the mineralocorticoid receptor, and differential co-factor binding. Our objective was to define the pharmacodynamic response of serum proteins to vamorolone. METHODS: Clinical trial participants with Duchenne muscular dystrophy (4 to <7 yr; n = 39; mean [SD] age = 5.3 [1.0]) enrolled in a multiple ascending dose study of vamorolone were studied (24-fold dose range). Dose-response and exposure-response of 1,305 serum proteins were defined by intra-subject longitudinal changes (baseline vs. Day 14). RESULTS: Dose-response analysis identified 159 of 1,305 serum proteins as dose-responsive to vamorolone (12 % of proteins tested; 20 % increased, 80 % decreased). Two inflammatory networks showed drug-responsive suppression. One centered on extracellular serine proteases and lymphotoxins (PI3, KLK7, KLK8, KLK11, lymphotoxins A, B) converging on NFκB. The second centered on cytokines (CCL22/MDC, CCL21, CCL14, CXCL12) and IL23 signaling. In the IL23 network, acutely responsive anti-inflammatory proteins included increases of an inhibitor of IL17 signaling (IL17RC) and decreases of IL23 (IL12B:IL23A). A protein associated with resistance to environmental microbes, PTP1C, showed strong induction and is a novel candidate for aspects of corticosteroid efficacy. Two networks of cell-associated proteins were identified as drug responsive that may represent muscle tissue response (efficacy): connective tissue remodeling upstream of Notch signaling, and plasma membrane proteins impinging on AKT1 signaling. CONCLUSION: The serum proteome pharmacodynamics of the response to vamorolone was defined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".