PARP Inhibitors in Cancer Therapy: Magic Bullets but Moving Targets
Bibliographic record
Abstract
The pharmacological inhibitors of \npoly(ADP-ribose) polymerase-1 (PARP- \n1) have reached the first milestone toward \ntheir inclusion in the arsenal of anti-cancer \ndrugs by showing consistent benefits \nin clinical trials against BRCA-mutant \ncancers that are deficient in the homologous \nrecombination repair (HRR) of \nDNA double strand breaks (DSB) (1, \n2). PARP inhibitors (PARPi) also potentiate \ntherapeutic efficacy of ionizing \nradiation and some chemotherapeutic \nagents (1). These effects of PARPi \nwere initially linked to inhibition of the \nrole of PARP-1 in base excision repair \n(BER) of DNA damaged by endogenous \nor exogenous agents, resulting in \naccumulation of single strand breaks \n(SSB), which upon conversion to toxic \nDSB lesions would kill cancer cells deficient \nin DSB repair (1, 3, 4). However, \nPARPi lethality in HRR-deficient cancers \ncan also be explained by other mechanisms \nnot involving a direct effect of \nPARPi on BER [reviewed in Ref. (5, \n6)]. In addition, therapeutic benefits \nof PARPi with agents such as carboplatin \nin HRR-proficient and -deficient \ntumors [reviewed in Ref. (1, 7)], simply \ncannot be explained by BER inhibitory \neffect of PARPi. Therefore, PARPi are \nlike magic bullets that can kill cancer \ncells under different circumstances, \nbut to comprehend their global scope \nand limitations, here we discuss the full \nrange of their targets and the possible \nimpact of broad specificity of current \nPARPi during prolonged therapy of cancer \npatients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".