PARP inhibitor resistance mechanisms and PARP inhibitor derived imaging probes
Bibliographic record
Abstract
INTRODUCTION: Poly(ADP-ribose) polymerase 1 (PARP1) inhibition has become a major target in anticancer therapy. While PARP inhibitors (PARPi) are approved for homologous recombination (HR) deficient cancers, therapeutic resistance is a challenge and PARPi are now being investigated in cancers lacking HR deficiencies. This creates a need to develop molecular and imaging biomarkers of PARPi response to improve patient selection and circumvent therapeutic resistance. AREAS COVERED: PubMed and clinicaltrials.gov were queried for studies on PARPi resistance and imaging. This review summarizes established and emerging resistance mechanisms to PARPi, and the current state of imaging and theragnostic probes for PARPi, including fluorescently labeled and radiolabeled probes. EXPERT OPINION: While progress has been made in understanding PARPi therapeutic resistance, clinical evidence remains lacking and relatively little is known regarding PARPi response outside of HR deficiencies. Continued research will clarify the importance of known biomarkers and resistance mechanisms in patient cohorts and the broader utility of PARPi. Progress has also been made in PARPi imaging, particularly with radiolabeled probes, and both imaging and theragnostic probes have now reached clinical validation. Reducing abdominal background signal from probe clearance will broaden their applicability, and improvements to molecular synthesis and radiation delivery will increase their utility.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".