Assessing the binding properties of the anti-PD-1 antibody landscape using label-free biosensors
Bibliographic record
Abstract
Here we describe an industry-wide collaboration aimed at assessing the binding properties of a comprehensive panel of monoclonal antibodies (mAbs) against programmed cell death protein 1 (PD-1), an important checkpoint protein in cancer immunotherapy and validated therapeutic target, with well over thirty unique mAbs either in clinical development or market-approved in the United States, the European Union or China. The binding kinetics of the PD-1/mAb interactions were measured by surface plasmon resonance (SPR) using a Carterra LSA instrument and the results were compared to data collected on a Biacore 8K. The effect of chip type on the SPR-derived binding rate constants and affinities were explored and the results compared with solution affinities from Meso Scale Discovery (MSD) and Kinetic Exclusion Assay (KinExA) experiments. When using flat chip types, the LSA and 8K platforms yielded near-identical kinetic rate and affinity constants that matched solution phase values more closely than those produced on 3D-hydrogels. Of the anti-PD-1 mAbs tested, which included a portion of those known to be in clinical development or approved, the affinities spanned from single digit picomolar to nearly 425 nM, challenging the dynamic range of our methods. The LSA instrument was also used to perform epitope binning and ligand competition studies which revealed over ten unique competitive binding profiles within this group of mAbs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".