CSF proteomic analysis of semorinemab Ph2 trials in prodromal-to-mild (Tauriel) and mild-to-moderate (Lauriet) Alzheimer’s disease identifies distinct trial cell-type specific proteomic signatures
Bibliographic record
Abstract
Abstract Targeting of tau pathology has long been proposed as a potential therapeutic strategy for Alzheimer’s disease (AD). Semorinemab is a humanized IgG4 monoclonal antibody that binds to all known isoforms of full-length tau with high affinity and specificity. Semorinemab’s safety and efficacy have been studied in two Phase 2 randomized, double-blind, placebo-controlled, parallel-group clinical trials: Tauriel (prodromal-to-mild AD; NCT03289143 ) and Lauriet (mild-to-moderate AD; NCT03828747 ). CSF was collected from a subset of patients at baseline and after 49 or 73 weeks in Tauriel and baseline and after 49 or 61 weeks in Lauriet. We generated a large proteomics dataset, using more than 250 cerebrospinal fluid (CSF) samples and detecting more than 3500 proteins, to investigate the effects of semorinemab in each trial. Treatment-induced proteomic signatures were defined for each study as a set of proteins significantly elevated in the treatment arm in the respective study. Integration of the corresponding gene signatures with two independent brain single-nucleus RNA-seq datasets from AD and healthy aged controls revealed that Lauriet signature genes were enriched in microglial cells, while Tauriel signature genes were more broadly expressed across major brain cell types. Furthermore, the Lauriet trial gene signature was significantly upregulated in microglia from AD patients as compared to non-demented controls. The elevation of proteins such as CHI3L1 and GPNMB with treatment suggested an activated glial state. Taken together, this study utilizes a large CSF clinical proteomics dataset to assess the pharmacodynamic response of semorinemab and contributes to our understanding of how an anti-tau antibody influences disease-relevant pathophysiology in AD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".