Abstract 4203: Discovery and development of a potent and highly selective ATR inhibitor IMP9064
Bibliographic record
Abstract
Abstract The ataxia telangiectasia and RAD3-related (ATR) kinase is a crucial component of the DNA damage response (DDR) and functions in conjunction with ataxia telangiectasia mutated (ATM). Loss or functional deficiency of ATM may lead to increased reliance on ATR signaling pathways. Preclinical and clinical studies have indicated a potential synthetic lethality between ATR inhibition and ATM deficiency. We have identified a novel, potent, and highly selective ATR inhibitor IMP9064. Herein we present its discovery and IND enabling in vitro and in vivo studies. IMP9064 is highly activity inhibiting ATR, and is highly selective among kinases. IMP9064 also shows potent cytotoxicity to a wide range of cancer cell lines. In addition, IMP9064 has a desirable PK profile in preclinical species. IMP9064 has demonstrated anti-tumor efficacy in human colorectal cancer CDX animal models with good dose-response tumor growth inhibition and tolerability. The results of in vitro and in vivo studies suggest that used alone or in combination with PARP inhibitor, WEE1 inhibitor, PKMYT1 inhibitor or HER2 ADC, IMP9064 has good anti-tumor activity and synergistic effect. IMP9064 has entered a phase 1/2 study to evaluate the safety and efficacy either as monotherapy or in combination with PARP inhibitor Senaparib in patients with advanced solid tumors (ClinicalTrials.gov Identifier: NCT05269316). The recommended phase 2 dose (RP2D) has been determined and IMP9064 is currently in expansion studies for selected tumors. Citation Format: Sui Xiong Cai, Ning Ma, Xiaozhu Wang, Yangzhen Jiang, Mingchuan Guo, Ruiyu Zhou, Mu Chen, Ye Edward Tian. Discovery and development of a potent and highly selective ATR inhibitor IMP9064 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4203.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".