Assessing Pharmacogenomic <i>loci</i> Associated with the Pharmacokinetics of Vamorolone in Boys with Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Abstract Human genetic variation (polymorphisms) in genes coding proteins involved in the absorption, distribution, metabolism, and elimination (ADME) of drugs can have a strong effect on drug exposure and downstream efficacy and safety outcomes. Vamorolone, a dissociative steroidal anti‐inflammatory drug for treating Duchenne muscular dystrophy (DMD), primarily undergoes oxidation by CYP3A4 and CYP3A5 and glucuronidation by UDP‐glucuronosyltransferases. This work assesses the pharmacokinetics (PKs) of vamorolone and sources of interindividual variability (IIV) in 81 steroid‐naïve boys with DMD aged 4 to <7 years old considering the genetic polymorphisms of CYPS3A4 (CYP3A4*22, CYP3A4*1B), CYP3A5 (CYP3A5*3), and UGT1A1 (UGT1A1*60) utilizing population PK modeling. A one‐compartment model with zero‐order absorption (Tk0, duration of absorption), linear clearance (CL/F), and volume (V/F) describes the plasma PK data for boys with DMD receiving a wide range of vamorolone doses (0.25‐6 mg/kg/day). The typical CL/F and V/F values of vamorolone were 35.8 L/h and 119 L, with modest IIV. The population Tk0 was 3.14 h yielding an average zero‐order absorption rate (k0) of 1.16 mg/kg/h with similar absorption kinetics across subjects at the same vamorolone dose (i.e., no IIV on Tk0). The covariate analysis showed that none of the genetic covariates had any significant impact on the PKs of vamorolone in boys with DMD. Thus, the PKs of vamorolone is very consistent in these young boys with DMD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".