Abstract IA013: Discovery of the dual A2A/A2B receptor antagonist MK-1088 for the treatment of solid tumors
Bibliographic record
Abstract
Abstract In the tumor microenvironment (TME), adenosine levels have been shown to be elevated relative to normal tissues. This increase in adenosine levels renders an immunosuppressive effect via direct effects on T cells via agonism of the A2A receptor and indirect effects via agonism of both A2A and A2B receptors on myeloid cells. With these observations as a backdrop, we sought to develop a dual A2A/A2B receptor antagonist with properties that would enable maintenance of high levels of target engagement of the A2A and A2B receptors even at trough concentration. Drawing on our organization’s significant prior experience developing A2A receptor antagonists for the potential treatment of Parkinson’s disease, we developed MK-1088, a highly potent A2A/A2B dual receptor antagonist that was purposefully designed to possess excellent selectivity over the related A1 and A3 receptors. This presentation will detail the discovery and development strategy that was employed to identify molecules that met the profile exemplified by this molecule. A single ascending dose study of MK-1088 in healthy human volunteers demonstrated our ability to achieve, at trough concentration, >99% target engagement (TE) at the A2A receptor and >90% TE at the A2B receptor. Details of the pharmacokinetics, safety, and tolerability from this study will be highlighted. Citation Format: Duane E. DeMong, Sheila Ranganath, Jared Cumming, Matthew Larsen, Yonglian Zhang, Christopher Plummer, Amjad Ali, Anthony Palmieri, Evan Barry, Pierre Daublain, Pranav Gupta, Manash Chatterjee, Jeremy Presland, Sebastian Schneider, Paul Ciaccio, Daniel Tatosian, Aaron Sather, Ben Turnbull, Steven Silverman, Harry Chobanian, Harini Krishnamurthy, Richard Wnek, Stephen Crowley, Alita Miller, Mark Ayers, Marlene Hinton, Jill Chrencik, Sylvie Rottey, Jennifer O'Neil. Discovery of the dual A2A/A2B receptor antagonist MK-1088 for the treatment of solid tumors [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 IA013.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".