Development and validation of an ELISA-based method for determining neutralizing antibodies to pembrolizumab in human serum based on inhibition of the drug binding to its PD-1 target
Bibliographic record
Abstract
Monoclonal antibodies (mAbs) are potentially able to trigger undesired humoral immune responses in the patients and develop ADA (anti-drug antibody) to the protein drugs. Neutralizing anti-drug antibodies are among the main factors affecting safety and effectiveness of the therapy. If it is impossible to apply cell-based tests to determine neutralizing antibodies, competitive ligand binding assay may be used as an alternative. Pembrolizumab (Pembro) is a broad-spectrum antitumor drug, being a humanized IgG4 kappa antibody to the programmed cell death receptor-1 (PD-1) that blocks interaction of this receptor with its ligands PD-L1 and PD-L2. Due to some technical issues, cell culture test is not feasible for Pembro, due to high risk of obtaining unreliable results. The aim of our study was to develop and validate a method for detection of neutralizing antibodies to Pembro in human serum based on inhibition of pembrolizumab binding to its PD-1 target. The experimental drug pembrolizumab RPH-075 (R-Pharm) was used in the study. Anti-Pembrolizumab antibodies KRIBIOLISATM Anti-Pembrolizumab (KEYTRUDA®) ELISA, India) were used as a positive control sample for neutralizing antibodies. Determination of antibodies was carried out by ELISA technique using acid dissociation of the immune complex and the Affinity capture elution (ACE) technique. The ELISA method was validated by the following characteristics: selectivity, sensitivity, specificity, “hook” effect, drug tolerance, precision. Due to the use of sample pretreatment approaches (ACE technique) for analysis of neutralizing antibodies, a sensitivity level of 100 ng/mL was achieved in the presence of pembrolizumab at 40 μg/mL. In this paper, a method was substantiated by calculating the cutoff point, sensitivity, and selectivity based on ROC analysis and floating exclusion limit (PSCP) through the average values of optical density NC and LPC in each individual analytical cycle. The developed method for determining neutralizing antibodies to pembrolizumab may be used to assess the undesirable immunogenicity of pembrolizumab at the stage of 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 | 0.003 |
| 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".