Binding and functional profiling of antibody mutants guides selection of optimal candidates as antibody drug conjugates
Bibliographic record
Abstract
An increasingly appreciated conundrum in the discovery of antibody drug conjugates (ADCs) is that an antibody that was selected primarily for strong binding to its cancer target may not serve as an optimal ADC. In this study, we performed mechanistic cell-based experiments to determine the correlation between antibody affinity, avidity, internalization and ADC efficacy. We used structure-guided design to assemble a panel of antibody mutants with predicted Her2 affinities ranging from higher to lower relative to the parent antibody, Herceptin. These antibodies were ranked for binding via SPR and via flow-cytometry on high-Her2 SKOV3 cells and low-Her2 MCF7 cells, the latter acting as a surrogate for low-Her2 normal cells. A subpanel of variants, representative of different Her2-binding affinities (2 strong, 2 moderate and 3 weak), were further screened via high-content imaging for internalization efficacies in high versus low-Her2 cells. Finally, these antibodies were evaluated in ADC cytotoxicity screening assays (using DM1 and MMAE secondary antibodies) and as antibody-drug conjugates (DM1 and PNU159682). Our results identified specific but weak Her2-binding variants as optimal candidates for developing DM1 and PNU ADCs since they exhibited high potencies (low to sub-nM) in high-Her2 SKOV3 cells and low toxicities in low-Her2 cells. The 2 strong-affinity variants were highly potent in SKOV3 cells but also showed significant toxicities in low-Her2 cells and therefore are predicted to be toxic in normal tissues. Our findings show that pharmacological profiling of an antibody library in multiple binding and functional assays allows for selection of optimal ADCs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".