Development and Validation of the ELISA Method for Anti-trastuzumab Antibodies Determination in Human Serum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction. One of the widely used specific anti-HER 2 (human epidermal growth factor receptor 2) MAb drugs is trastuzumab. Trastuzumab is highly effective for malignant HER 2 hyperexpression reduction, which results in HER 2 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it