An Assay to Monitor the Engagement and Modulation of CD6 on T cells as a Clinical Biomarker of Treatment with Itolizumab
Bibliographic record
Abstract
Abstract Itolizumab is a novel first-in-class monoclonal antibody that selectively targets the co-stimulatory molecule CD6, a receptor that is highly expressed on CD4 and CD8 T cells and plays an important role in activation and migration. Monitoring target engagement and changes in receptor levels is critically important to interpreting clinical data. To evaluate the pharmacodynamic properties of itolizumab treatment on T cells in patients including those with graft versus host disease (GvHD), Precision for Medicine has developed and validated a 10-color flow cytometry assay to assess the engagement and modulation of cell-surface CD6. The assay was validated using whole blood from healthy donors. Blood was spiked with five concentrations of drug selected based on expected PK in the clinical study. The assay conditions were optimized for sensitivity, signal: noise ratio, detection of free receptor, receptor-bound itolizumab and total surface CD6. Itolizumab was detected using a fluorochrome conjugated α-human IgG1 antibody while total surface CD6 expression was assessed using an antibody to a non-competing site on CD6. For validation, pre-set criteria were used to assess inter-assay, intra-assay, inter-operator precision and post-staining stability. Technical validation was successfully met; and the assay performs within acceptable precision parameters. Measuring cell-based receptor engagement and fate in patients on immuno-modulatory therapies is very challenging. This assay was designed and validated to be both sensitive and selective in the quantification of CD6 receptor occupancy and modulation to facilitate the determination of an optimal therapeutic dose in autoimmune and inflammatory diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".