Differences in Durability of PARP Inhibition by Clinically Approved PARP Inhibitors: Implications for Combinations and Scheduling
Bibliographic record
Abstract
Six PARP inhibitors (PARPi) are approved for cancer therapy as monotherapy agents in daily or twice-daily continuous dosing schedules to maintain the necessary continuous suppression of PARP activity. Continuous PARP inhibition is required for single-agent anticancer activity. To investigate if such intense schedules are necessary, we determined the durability of PARP inhibition up to 72 h after a 1 h pulse of 1 µM of five of the approved PARPi, rucaparib, olaparib, niraparib, talazoparib and pamiparib, in IGROV-1 and ES-2 (human ovarian cancer) cells. Rucaparib caused the most persistent inhibition of PARP activity when maintained at ≥75% at 72 h after drug withdrawal in both IGROV-1 and ES-2 cells, but inhibition was more rapidly lost with the other PARPi. PARPi are also under clinical investigation with ATR inhibitors, and thus, we evaluated the implications for scheduling with an ATR inhibitor (VE-821). Rucaparib enhanced VE-821 cytotoxicity in co-exposure, sequential and delayed (24 h drug-free) schedules in IGROV-1 and ES-2 cells. Olaparib and niraparib enhanced VE-821 cytotoxicity only in co-exposed cells and not in sequential exposures. These data have clinical implications for the scheduling of PARPi as a monotherapy and in combination with ATR inhibitors and other cytotoxic drugs.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".