Abstract PR003: Structure-guided design and optimization of small molecule CD73 inhibitors with excellent drug-like properties: discovery of quemliclustat
Bibliographic record
Abstract
Abstract In the tumor microenvironment, high concentrations of extracellular adenosine promote tumor proliferation through various immunosuppressive mechanisms. High expression of CD73—an ecto-nucleotidase that produces adenosine from adenosine monophosphate—has been associated with poorer prognosis in several tumor types. Herein, we describe the drug discovery efforts that resulted in the discovery of quemliclustat, a highly potent and selective inhibitor of CD73. As a small molecule with excellent drug-like properties, quemliclustat offers several advantages over CD73 antibodies in development, such as greater inhibition of CD73 enzymatic activity (both soluble and membrane-bound), deeper tumor penetration, and the potential for both IV and oral formulations. Quemliclustat was the first small molecule CD73 inhibitor to enter clinical development and is currently being evaluated in various Phase 1/2 studies in advanced solid tumors, including lung and pancreatic cancers. Recent data in metastatic pancreatic cancer are supportive of further study in this disease setting. Citation Format: Jenna Jeffrey Structure-guided design and optimization of small molecule CD73 inhibitors with excellent drug-like properties: discovery of quemliclustat. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr PR003
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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) | 0.000 | 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 | 0.000 |
| 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".