Comprehensive in vitro characterization of PD-L1 small molecule inhibitors
Bibliographic record
Abstract
Blockade of the programmed cell death 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) interaction has emerged as a powerful strategy in cancer immunotherapy. Recently, there have been enormous efforts to develop potent PD-1/PD-L1 inhibitors. In particular, Bristol-Myers Squibb (BMS) and Aurigene Discovery Technologies have individually disclosed several promising PD-1/PD-L1 inhibitors, whose detailed experimental data are not publicly disclosed. In this work, we report the rigorous and systematic in vitro characterization of a selected set of potent PD-1/PD-L1 macrocyclic peptide (BMSpep-57) and small-molecule inhibitors (BMS-103, BMS-142) from BMS and a peptidomimetic small-molecule inhibitor from Aurigene (Aurigene-1) using a series of biochemical and cell-based assays. Our results confirm that BMS-103 and BMS-142 are strongly active in biochemical assays; however, their acute cytotoxicity greatly compromised their immunological activity. On the other hand, Aurigene-1 did not show any activity in both biochemical and immunological assays. Furthermore, we also report the discovery of a small-molecule immune modulator, whose mode-of-action is not clear; however, it exhibits favorable drug-like properties and strong immunological activity. We hope that the results presented here will be useful in guiding the development of next-generation PD-1/PD-L1 small molecule inhibitors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".