Quantitative relationship between tominersen concentrations in cerebrospinal fluid and biomarker changes in Huntington's disease patients
Bibliographic record
Abstract
AIM: Intrathecally administered antisense oligonucleotide tominersen aims to slow Huntington's disease progression by lowering mutant huntingtin (mHTT) protein levels. This study used non-linear mixed effects population pharmacokinetic and pharmacodynamic (PKPD) modelling to characterize the relationship between tominersen concentration in cerebrospinal fluid (CSF) and CSF mHTT reduction. Additionally, the relationship between tominersen CSF exposure and changes in other CSF biomarkers was investigated to understand tominersen's pharmacodynamic profile. Finally, PKPD model simulations were conducted to inform the dose selection in the GENERATION HD2 study. METHODS: Data from four clinical studies, including 915 participants receiving placebo or tominersen doses (30-120 mg) every 4, 8 or 16 weeks for up to 25 months, were used to develop the PKPD model. The model was utilized to predict tominersen CSF exposure metrics for individual patients in the GENERATION HD1 study for the exposure-response (ER) analysis and to simulate the PK and PD profiles for lower doses. RESULTS: ) of 4.18 ng/mL. The ER analysis revealed that the highest exposure quartile showed a 54% mHTT reduction at steady state and transient elevations in biomarkers of neuroinjury and inflammation. In contrast, the lowest exposure quartile had a 24% mHTT reduction and a favourable biomarker profile. CONCLUSIONS: The PKPD model quantitatively confirms the relationship between tominersen exposure and CSF mHTT lowering. The ER analysis suggests that lower tominersen exposure levels may offer a better benefit-risk profile.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".