Development and Validation of the ELISA Method for Neutralizing Anti-trastuzumab Antibodies Detection in Human Blood Serum
Bibliographic record
Abstract
Introduction. Trastuzumab is the first known anti-HER2 agent, which revolutionized the treatment of one of the most common cancer types – breast cancer. Despite trastuzumab being approved long time ago, further improvement of related analytical methods remains relevant primarily due to the emergence of new biosimilars. For instance, immunogenicity – adverse reaction which is usually associated with biological drugs, can still be relevant for trastuzumab. Anti-drug antibodies, including neutralizing antibodies, caused by trastuzumab therapy, can affect drug effectiveness and safety profile. Aim. The aim of this study was to develop and validate the analytical method for neutralizing anti-trastuzumab antibodies determination in human blood serum. Materials and methods. The neutralizing anti-trastuzumab antibody determination was carried out by the competitive ELISA method, using spectrophotometric detection in the visible range of the spectrum. Results and discussion. The developed method was validated for cut-point, selectivity, sensitivity, specificity, precision and stability (short-term and long-term). To decrease the background noise from non-specific binding of sera components, the minimum required dilution value was determined at 0.5 % serum. The calculated value for cut-point was 14.62 %. The sensitivity of the developed method was estimated at 1985.2 ng/mL of neutralizing anti-trastuzumab antibodies. Conclusion. The obtained results allowed us to apply the developed ELISA method for the neutralizing anti-trastuzumab antibodies determination in human blood serum during trastuzumab immunogenicity assessment in bioequivalence clinical trials.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".