Generation and characterization of LPA-KIV9, a murine monoclonal antibody binding a single site on apolipoprotein (a)
Bibliographic record
Abstract
Lipoprotein (a) [Lp(a)] is a risk factor for CVD and a target of therapy, but Lp(a) measurements are not globally standardized. Commercially available assays generally use polyclonal antibodies that detect multiple sites within the kringle (K)IV 2 repeat region of Lp(a) and may lead to inaccurate assessments of plasma levels. With increasing awareness of Lp(a) as a cardiovascular risk factor and the active clinical development of new potential therapeutic approaches, the broad availability of reagents capable of providing isoform independence of Lp(a) measurements is paramount. To address this issue, we generated a murine monoclonal antibody that binds to only one site on apo(a). A BALB/C mouse was immunized with a truncated version of apo(a) that contained eight total KIV repeats, including only one copy of KIV 2 . We generated hybridomas, screened them, and successfully produced a KIV 2 -independent monoclonal antibody, named LPA-KIV9. Using a variety of truncated apo(a) constructs to map its binding site, we found that LPA-KIV9 binds to KIV 9 without binding to plasminogen. Fine peptide mapping revealed that LPA-KIV9 bound to the sequence 4076 LETPTVV 4082 on KIV 9 . In conclusion, the generation of monoclonal antibody LPA-KIV9 may be a useful reagent in basic research studies and in the clinical application of Lp(a) measurements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".