Safety, pharmacokinetics, and preliminary assessment of efficacy of mecasermin (recombinant human IGF-1) for the treatment of Rett syndrome
Bibliographic record
Abstract
Rett syndrome (RTT) is a severe X-linked neurodevelopmental disorder mainly affecting females and is associated with mutations in MECP2, the gene encoding methyl CpG-binding protein 2. Mouse models suggest that recombinant human insulin-like growth factor 1 (IGF-1) (rhIGF1) (mecasermin) may improve many clinical features. We evaluated the safety, tolerability, and pharmacokinetic profiles of IGF-1 in 12 girls with MECP2 mutations (9 with RTT). In addition, we performed a preliminary assessment of efficacy using automated cardiorespiratory measures, EEG, a set of RTT-oriented clinical assessments, and two standardized behavioral questionnaires. This phase 1 trial included a 4-wk multiple ascending dose (MAD) (40-120 μg/kg twice daily) period and a 20-wk open-label extension (OLE) at the maximum dose. Twelve subjects completed the MAD and 10 the entire study, without evidence of hypoglycemia or serious adverse events. Mecasermin reached the CNS compartment as evidenced by the increase in cerebrospinal fluid IGF-1 levels at the end of the MAD. The drug followed nonlinear kinetics, with greater distribution in the peripheral compartment. Cardiorespiratory measures showed that apnea improved during the OLE. Some neurobehavioral parameters, specifically measures of anxiety and mood also improved during the OLE. These improvements in mood and anxiety scores were supported by reversal of right frontal alpha band asymmetry on EEG, an index of anxiety and depression. Our data indicate that IGF-1 is safe and well tolerated in girls with RTT and, as demonstrated in preclinical studies, ameliorates certain breathing and behavioral abnormalities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".