DNA Repair Inhibitors: The Next Major Step to Improve Cancer Therapy
Bibliographic record
Abstract
Modern cancer therapies, mainly ionizing radiation and certain classes of chemotherapies target DNA. Although these treatments disrupt the genome, their rationale is clear. They prevent cancer cells from dividing and proliferating. Nevertheless, cancer cells can survive by over-activating a wide range of DNA repair pathways to eliminate the induced damage. In this context, DNA repair mechanisms are considered to be a vital target to improve cancer therapy and reduce the resistance to many DNA damaging agents currently in use as standard-of-care treatments. Here, we focus on two important DNA repair pathways, namely base excision repair (BER) and nucleotide excision repair (NER). Specifically, our focus is on two protein targets that are linked to the hallmark "relapse" and "drug resistance" phenomena. These are Excision Repair Cross-Complementation Group 1 (ERCC1), and DNA polymerase beta (pol β). The former is a key player in NER, while the latter is the error-prone polymerase of BER. Our objective is to list all known inhibitors for the two targets and provide an overview of the great efforts that were made in their discovery. While in the DNA pol β case more than sixty inhibitors were identified, very few inhibitors have been discovered on the ERCC1 side. It is hoped that this review will assist in the discovery of novel, potent and specific drug candidates aimed at improving existing cancer therapies including ionizing radiation, bleomycin, monofunctional alkylating agents and cisplatin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".