Interim phase 1 part A results for ALN‐APP, the first investigational RNAi therapeutic in development for Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: . ALN-APP is an investigational intrathecally (IT) administered RNAi therapeutic designed to reduce upstream intracellular and extracellular amyloid precursor protein (APP) levels by lowering APP mRNA. As a result, we hypothesize that ALN-APP may alter the cascade of events that result in neurodegeneration, potentially slowing, halting, or reversing Alzheimer's disease progression. METHOD: ALN-APP-001 (NCT05231785) Part A is an ongoing randomized, double-blind, placebo-controlled, Phase 1 single-ascending dose study in patients with EOAD. Patients are required to have disease onset at age <65 years, Clinical Dementia Rating® global score of 0.5 or 1.0, and Mini Mental State Examination score >20. Patients are being evaluated over 6 months, with further follow-up of up to 6 months as needed. The primary endpoint is the safety and tolerability of ALN-APP. Secondary objectives include the evaluation of pharmacokinetics and pharmacodynamic effects of ALN-APP. RESULT: 12 patients were enrolled and randomized 2:1 to receive ALN-APP or placebo in 25mg and 75mg dose cohorts. Baseline characteristics are shown in Table 1. Mean (SD) duration on study was 6.7 (1.7) months for cohort 1 (25mg) and 2.0 (1.0) months for cohort 2 (75mg). Dose-dependent reductions of soluble APPα and APPβ (sAPPα and sAPPβ) levels in cerebrospinal fluid (CSF) at day 15 were observed following a single dose of ALN-APP, with mean reductions from baseline of 55% (sAPPα) and 69% (sAPPβ), and maximum reductions of 71% (sAPPα) and 83% (sAPPβ) in the 75mg cohort (n = 4) (Table 2). All adverse events (AEs) by data cut-off on 01/17/2023 were mild or moderate (Table 3), with no AEs deemed related to study drug by the investigators. Additional cohort data will be presented at the meeting. CONCLUSION: This first clinical study of a CNS-administered RNAi therapeutic demonstrates target engagement of APP, with reductions in CSF sAPPα and sAPPβ. To date, ALN-APP remains generally well tolerated with all reported AEs mild or moderate. These interim results support further evaluation of ALN-APP in patients with EOAD. Reference: 1. Hampel H et al. Molecular Psychiatry (2021). 26:5481-5503.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".