Measuring Lp(a) particles with a novel isoform-insensitive immunoassay illustrates efficacy of muvalaplin
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)] is a cardiovascular risk factor, and there is considerable interest in developing Lp(a)-lowering therapeutics for cardiovascular prevention. Current commercial Lp(a) assays measure total apolipoprotein(a) [apo(a)] and may be insufficient to accurately measure Lp(a) concentrations and determine Lp(a) lowering by a new class of small-molecule Lp(a) formation inhibitors such as muvalaplin. We developed a novel immunoassay that measures only Lp(a) particles. This intact Lp(a) assay demonstrated robust analytical performance, was insensitive to apo(a) isoform size, and correlated with a liquid chromatography-tandem mass spectrometry method. Muvalaplin phase I multiple ascending dose study samples and lepodisiran, a small-interfering RNA that lowers Lp(a), phase I single ascending dose study samples were analyzed using the intact Lp(a) assay and commercial assays. The Lp(a)-lowering efficacy of muvalaplin was underestimated by the commercial assay measuring total apo(a) compared with the intact Lp(a) assay specifically measuring Lp(a) particles. In contrast, the Lp(a)-lowering effect of lepodisiran was clinically comparable between the intact Lp(a) assay and commercial assay. This novel intact Lp(a) assay provides a more accurate approach for the assessment of Lp(a)-lowering agents and the study of Lp(a)-associated risk compared with currently available assays.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".