Abstract 4961: Novel biased PAR2 inhibitors with best-in-class properties reduce resistance to both chemotherapy and immunotherapy in oncology models
Bibliographic record
Abstract
Abstract A large-scale meta-analysis has recently identified Protease-activated receptor 2 (PAR2) gene expression to be significantly associated with resistance to immune checkpoint blockade (ICB) in cancer patients and preclinical models. PAR2 and its ligands (proteases) are indeed upregulated in different cancer types and are expressed by various cells in the tumor microenvironment. In cancer cells, the PAR2 receptor controls cell migration, proliferation, survival, and expression of inflammatory cytokines. In immune cells, it influences the infiltration and phenotype of macrophages and T cells. Therefore, PAR2 represents a promising therapeutic target in oncology and immuno-oncology. A novel series of potent and selective PAR2 inhibitors has been developed at Domain Therapeutics. In vitro experiments demonstrated unique properties of our PAR2 small molecule antagonists when compared to those of competitors. The antagonists inhibit pathogenic signaling pathways (i.e. Gz, G13, Gq, G14 and G15 protein activation as well as intracellular calcium) but not βarrestin2 recruitment, potentially reducing the risks of drug resistance. Furthermore, they maintain high potency and insurmountability in conditions that mimic the tumor microenvironment (high concentration of proteases and acidic pH). Finally, their pharmacokinetic properties are compatible with a once-a-day oral administration and demonstrate no signs of in vivo toxicity except at high doses (>500 mg/kg). Proof-of-concept experiments showed that PAR2 antagonists prevented PAR2-mediated resistance to Gefitinib in vitro and increased the potency of anti-PD1 therapy in vivo in pre-clinical syngeneic mouse models. Immunohistochemistry analyses from cancer patient biopsies confirmed that high expression levels of PAR2 in cancer and stromal cells within the tumor microenvironment significantly associates with the patient overall survival. In conclusion, new potent and selective negative allosteric modulators of PAR2 have been developed, they demonstrate strong potency by alleviating the resistance to both chemo- and immunotherapy in cancer models. These findings confirm the high value of PAR2 as a therapeutic target and demonstrates the relevance of small molecule inhibitors targeting this receptor to treat cancer. Citation Format: Thibaut Brugat, Francesco Bergami, Baptiste Rugeri, Aurélie Janvier, Edith Steinberg, Luc Baron, Mandy Recolet, Xavier Wirth, Meriem Semache, Antoine Mousson, Camille Dietsch, Quentin Ruet, Orphée Blanchard, Célia Jacoberger-Foissac, Isabelle Cousineau, Maleck Kadiri, Anne-Laure Blayo, Christel Franchet, Stanislas Mayer, John Stagg, Nathalie Lenne, Stephan Schann. Novel biased PAR2 inhibitors with best-in-class properties reduce resistance to both chemotherapy and immunotherapy in oncology models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4961.
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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) | 0.000 | 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) | 0.006 | 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".