An Integrative Approach to Inform Optimal Administration of OX40 Agonist Antibodies in Patients with Advanced Solid Tumors
Bibliographic record
Abstract
Abstract Purpose: The success of checkpoint blockade has led to a significant increase in the development of a broad range of immunomodulatory molecules for the treatment of cancer, including agonists against T-cell costimulatory receptors, such as OX40. Unlike checkpoint blockade, where complete and sustained receptor saturation may be required for maximal activity, the optimal dosing regimen and receptor occupancy for agonist agents is less well understood and requires further study. Experimental Design: We integrated both preclinical and clinical biomarker data sets centered on dose, exposure, receptor occupancy, receptor engagement, and downstream pharmacodynamic changes to model the optimal dose and schedule for the OX40 agonist antibody BMS-986178 alone and in combination with checkpoint blockade. Results: Administration of the ligand-blocking anti-mouse surrogate antibody OX40.23 or BMS-986178 as monotherapy or in combination with checkpoint blockade led to increased peripheral CD4+ and CD8+ T-cell activation in tumor-bearing mice and patients with solid tumors, respectively. OX40 receptor occupancy between 20% and 50% both in vitro and in vivo was associated with maximal enhancement of T-cell effector function by anti-OX40 treatment, whereas a receptor occupancy > 40% led to a profound loss in OX40 receptor expression, with clear implications for availability for repeat dosing. Conclusions: Our results highlight the value of an integrated translational approach applied during early clinical development to aggregate preclinical and clinical data in an effort to define the optimal dose and schedule for T-cell agonists in the clinic.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".