A Novel Bioassay for the Activity Determination of Therapeutic Human Brain Natriuretic Peptide (BNP)
Bibliographic record
Abstract
BACKGROUND: Recombinant human brain natriuretic peptide (rhBNP) is an important peptide-based therapeutic drug indicated for the treatment of acute heart failure. Accurate determination of the potency of therapeutic rhBNP is crucial for the safety and efficacy of the drug. The current bioassay involves use of rabbit aortic strips, with experiments being complicated and time-consuming and markedly variable in results. Animal-less methods with better precision and accuracy should be explored. We have therefore developed an alternative cell-based assay, which relies on the ability of BNP to induce cGMP production in HEK293 cells expressing BNP receptor guanylyl cyclase-A. METHODOLOGY/PRINCIPAL FINDINGS: An alternative assay based on the measurement of BNP-induced cGMP production was developed. Specifically, the bioassay employs cells engineered to express BNP receptor guanylyl cyclase-A (GCA). Upon rhBNP stimulation, the levels of the second messager cGMP in these cells drastically increased and subsequently secreted into culture supernatants. The quantity of cGMP, which corresponds to the rhBNP activity, was determined using a competitive ELISA developed by us. Compared with the traditional assay, the novel cell-based assay demonstrated better reproducibility and precision. CONCLUSION/SIGNIFICANCE: The optimized cell-based assay is much simpler, more rapid and precise compared with the traditional assay using animal tissues. To our knowledge, this is the first report on a novel and viable alternative assay for rhBNP potency analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".