Flow Cytometry-Based Epitope Binning Using Competitive Binding Profiles for the Characterization of Monoclonal Antibodies against Cellular and Soluble Protein Targets
Why this work is in the frame
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Bibliographic record
Abstract
A key step in the therapeutic antibody drug discovery process is early identification of diverse candidate molecules. Information comparing antibody binding epitopes can be used to classify antibodies within a large panel, guiding rational lead molecule selection. We describe a novel epitope binning method utilizing high-throughput flow cytometry (HTFC) that leverages cellular barcoding or spectrally distinct beads to multiplex samples to characterize antibodies raised against cell membrane receptor or soluble protein targets. With no requirement for sample purification or direct labeling, the method is suited for early characterization of antibody candidates. This method generates competitive binding profiles of each antibody against a defined set of known or unknown reference antibodies for binding to epitopes of an antigen. Antibodies with closely related competitive binding profiles indicate similar epitopes and are classified in the same bin. These large, high-throughput, multiplexed experiments can yield epitope bins or clusters for the entire antibody panel, from which a conceptual map of the epitope space for each antibody can be created. Combining this valuable epitope information with other data, such as functional activity, sequence, and selectivity of binding to orthologs and paralogs, enables us to advance the best epitope-diverse candidates for further development.
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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 | 0.000 | 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 | 0.001 |
| 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