P.045 Trofinetide for the treatment of Rett syndrome: long-term safety and efficacy results from the open-label LILAC and LILAC-2 studies
Bibliographic record
Abstract
Background: Trofinetide significantly improved core symptoms of Rett syndrome (RTT) with an acceptable safety profile in LAVENDER. Here, we report the safety and efficacy results of LILAC and LILAC-2, open-label extension studies of LAVENDER. Methods: Females with RTT, aged 5–21 years, received twice-daily, oral trofinetide in LILAC for 40 weeks. Participants who completed LAVENDER and LILAC continued trofinetide in LILAC-2, a 32-month extension study. Safety assessments included the incidence of adverse events (AEs). Efficacy endpoints included the Rett Syndrome Behaviour Questionnaire (RSBQ) and the Clinical Global Impression–Improvement (CGI-I) scale. Results: Overall, 154 patients were enrolled in LILAC. The most common AEs were diarrhea (74.7%) and vomiting (28.6%). The mean (standard error [SE]) change from the LAVENDER baseline to Week 40 in the LILAC study in RSBQ was -7.3 (1.62) and -7.0 (1.61) for participants treated with trofinetide and placebo in LAVENDER, respectively. Mean (SE) CGI-I scores compared with the LILAC baseline at Week 40 were 3.1 (0.11) and 3.2 (0.14) for patients treated with trofinetide and placebo in LAVENDER, respectively. Similar safety and efficacy trends were observed in LILAC-2. Conclusions: Trofinetide continued to improve symptoms of RTT in LILAC and LILAC-2 with a safety profile consistent with LAVENDER.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".