Best practices for optimization and validation of flow cytometry‐based receptor occupancy assays
Why this work is in the frame
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Bibliographic record
Abstract
In the development of therapeutic compounds that bind cell surface molecules, it is critical to demonstrate the extent to which the drug engages its target. For cell-associated targets, flow cytometry is well-suited to monitor drug-to-target engagement through receptor occupancy assays (ROA). The technology allows for the identification of specific cell subsets within heterogeneous populations and the detection of nonabundant cellular antigens. There are numerous challenges in the design, development, and implementation of robust ROA. Among the most difficult challenges are situations where there is receptor modulation or when the target-antigen is expressed at low levels. When the therapeutic molecules are bi-specific and bind multiple targets, these challenges are increased. This manuscript discusses the challenges and proposes best practices for designing, optimizing, and validating ROA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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