Development and Validation of the ELISA Method for Anti-trastuzumab Antibodies Determination in Human Serum
Bibliographic record
Abstract
Introduction. One of the widely used specific anti-HER2 (human epidermal growth factor receptor 2) MAb drugs is trastuzumab. Trastuzumab is highly effective for malignant HER2 hyperexpression reduction, which results in HER2 oncogenicity decrease. As any other biotherapeutics trastuzumab can cause immunological adverse reactions, e.g. immunogenicity or anti-drug antibodies (ADAs) production. Aim. The aim of this study was to develop and validate the analytical method for anti-trastuzumab antibodies determination in human blood serum. Materials and methods. The semi-quantitative anti-trastuzumab antibody determination was carried out by the ELISA method combined with ACE technique, using spectrophotometric detection in the visible range of the spectrum. Results and discussion. The developed method was validated for cut point, selectivity, sensitivity, "hook" effect, drug tolerance, 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 10 % serum. The calculated values for screening cut point (normalization factor) and confirmatory cut point were 0.004 and 34.59 %, respectively. The sensitivity of the developed method was estimated at 99.5 ng/mL of anti-trastuzumab antibodies. Conclusion. The obtained results allow us to use the developed ACE ELISA method for the determination of anti-trastuzumab antibodies in human serum during trastuzumab safety 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".