Abstract 5604: Modulating DNA repair through endo-exonuclease inhibition: a new therapeutic paradigm in oncology
Bibliographic record
Abstract
Abstract DNA damage repair (DDR) mechanisms are crucial for the maintenance of genomic stability and are emerging as potential therapeutic targets for cancer. Endo-exonuclease (EE)1 plays a key role in this process and increase EE expression has been observed in the cancer cells examined. In this context, we identified a dicationic diarylfuran, pentamidine, which is an antiparasitic drug that has been used clinically to treat opportunistic infections such as Pneumocystis carinii. Pentamidine is an inhibitor of the endo-exonuclease as determined by enzyme kinetic assay and is known to be active on cancer cells. In a proof of concept study2, tumor with increased EE expression (determined by IHC) showed the best response to this drug. We have now identified novel mono- and di-amidine analogs of pentamidine that are more effective than the parent drug in both in vitro and in vivo studies. Furthermore, pre-clinical animal studies of our lead candidate, MTDX203, showed an increased anti-cancer activity with a higher safety index than pentamidine. Based on a predictive PK/PD model, a human iv infusion dose administered twice weekly at ~0.4 mpk (i.e. 28 mg/day, ~10% of pentamidine effective iv dose) is expected to be clinically effective. Our work establishes the EE as a therapeutic target for cancer and identifies a new First in Class of novel anti-cancer agents. 1Chow et al. (2004) MCT 3:911-9182 NCT01378143: Pentamidine (OCZ103-OS) in Patients With Unresectable and Locally Recurrent or Metastatic Colorectal Cancer Undergoing Standard Chemotherap Citation Format: Terry Chow, Jianmin Duan, Patrick Colin, Colin Bier. Modulating DNA repair through endo-exonuclease inhibition: a new therapeutic paradigm in oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5604.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".